Skills you must know before you step into a PhD

I wish I had someone like me in the past to tell me about the material presented in this article, to tell me what are the necessary set of skills that I definitely need to know so I can prepare and learn them before I begin to do a PhD. Furthermore, I wish I had someone like me in the past to tell me about the unwritten esoteric rules of academia, about PhDs tacit rules, about supervisors and inter-departmental politics and of course about the hypocrisy you might find even in the top universities in UK.

This article is extremely frank, down to earth and talks with guts so if you  don’t like that, well, I am afraid it is not suitable to you. I have been told by many lovely academics (staff, supervisors, lecturers) and by students from the University of St Andrews, University of Dundee, University of Edinburgh among many others, that my writing speaks truth without diplomacy or any pathetic sugar-coated language or any form of equivocation and I believe this is what the reader really wants (especially people who want to embark on a very long PhD). People are really tired these days of all the twisted fake and appease-like hedging language that leads nowhere and is pure non-sense and a waste of time. I have never ever imagined at all that this article and the article on literature review  are now recommended readings suggested by many supervisors/lecturers to their students.

I believe (it is my opinion here) – Yep! I do a lot of introspection & extrospection–  I believe that these articles being recommended is maybe due to the fact that my articles  remind many academics of their difficult time when they were PhD candidates and the suffering, the inter-departmental weird politics and the hypocrisy they had to deal with on a daily basis. I think these articles speak to a part of their academic souls which they liked and have nostalgia for (the part of your soul as an academic and researcher that you had when you started as a researcher with pure intent and “innocence”).

Before you start reading the article, you need to know with certitude that you really want to do a PhD! A PhD is not for everybody! There is this effect of “monkey imitating behaviour” among the masses where people do a PhD out of jealousy or they think if the neighbour did a PhD, waw! that must be awesome! maybe it is suitable also  for them to do one! It is not like that at all! I literally heard people telling me they are doing a PhD just for the sake of having a “Dr.” next their name on their credit card! Imagine!

This does not mean in anyway that a PhD requires a level above the average intelligence. Why am I saying this? I can tell you exactly what the hypocrites usually tell you about a PhD: “you will get a lot of transferable skills”,  “You will learn research skills”, “You will improve your prospects of employability and financial security” – this is wrong because academia compared to working in the industry is the poorest and most pathetic choice when it comes to financial security and achieving wealth.  I mean come on! you can get all those skills if you choose another path in Life. What is the real catch in a PhD? learning how to do research, how to write an academic paper, how to present your work in conferences and how to present to an audience! really! Let me tell you the truth without hypocrisy.  A PhD  is a very very very long  undertaking which spans a lot of years (3 to 5 years) of studying one particular tiny research topic (like eating the same food everyday) and where you should go to the edge of the human knowledge in this particular topic or field (i.e. at the end of your PhD you should supposedly be THE expert in this tiny particular research topic). Most commonly, a lot of people follow funding opportunities when it comes to research topics so they end up doing a research topic they are NOT very passionate about which is quite sad. You must be extremely passionate about your research topic to endure spending a lot of years studying it! It would be very beneficial to you to avoid doing a Ph.D in a topic that you are not passionate about just because you got a scholarship. That might sound understandably very blasphemous to a lot of people. Think of it as eating the worst repugnant food possible every single day for 3 to 5 years just because someone has offered you to pay the rent and a small monthly stipend for food! A lot of scholarships are not tied to a particular topic and they are usually offered by different schools/departments or by private organisations or by governmental institutions. This type of scholarships is ideal since if you got the money, you hold the power to choose the topic and the adviser who is expert in it.

A PhD candidate a.k.a PhD student will receive a fixed stipend living like a rat for 3 to 5 years compared to being in industry either owning a company or working in one where she/he will progress and earn hopefully more money.  I hate the usage of the term “student”, it is semantically incorrect especially when it is used to describe the research setting that mimics a PhD. Actually the consensus and the most correct usage is to use the term “PhD candidate”  and not “PhD student” and this is according to the UK Quality Assurance Agency for Higher Education [2] not me saying that. I will use whenever possible the term “candidate” throughout this article.

A PhD requires a lot of perseverance, diligence and determination and I mean a lot. You might reach stages where you want to give up. In addition, in a PhD you need to manage your time very efficiently since there are no deadlines except maybe your annual reviews, or papers submission deadlines and of course your thesis submission and Viva Voce at the end. So if you are a guy or a gal that needs deadlines and serious pressure to work well, you might find your life extremely difficult in a PhD.  The main role of a PhD is to make you an independent researcher and thinker.

In a PhD, You will meet a lot of good people, a lot of smart people and you will definitely meet a lot of hypocrites who mastered saying, writing and behaving in a fallacious way. The tools of the academic craft of many academics you encounter are ‘red herring’ and ‘straw man’ fallacies among many others in order for these specimens to survive in academia. I call it a show of PR of knowledge – pompous but empty at the end of the day. Eventually, your money/scholarship, your most precious years of your adult life… are in the hands and the mood/ethics of one individual (the supervisor/adviser) who governs the smoothness of your PhD. A PhD in my opinion is a pathetic endeavour to pursue knowledge, not because it is not noble in what it tries to achieve (i.e its aims), but because in its current form depends on one person’s mood, human nature, ethics and skills and depends a lot on inter-departmental politics in many cases. This is because you can research, discover awesome things and contribute to humanity’s knowledge as a free spirit outside a PhD. Do you see my point here? I am not discussing your competence and your own skills to do a PhD here and the work involved in it. It is not about how much hard work you put in sometimes this does not matter in many situations of hypocrisy. If you are accepted in a PhD that means you have already all the necessary aptitudes and skills yourself. The system is supposed to be meritocratic.

That has been said, if you are really convinced to do a PhD, you can start reading this article. Unfortunately, the SEO (Search Engine Optimisation) of this article is not that good yet, so if you land on this article from a search engine, you should get down on your knees and thank whatever deity you believe in. This article increased the traffic on my website of 450%, imagine! so I was forced to upgrade my hosting plan. Probably you landed on a lot of fluff and non-sense articles on the web precisely about what is dubbed as ‘transferable skills’ such as ‘leadership skills’ and all this non-sense. It is funny 🙂 to tell you the truth, those articles are the dumbest material, I have ever read. So according to them, you need to have “leadership skill’ as a skill for a PhD so you can exercise it on your moody and extremely insecure supervisor/adviser. Really! 🙂

You need something more down to earth advice, don’t you think? This article is tangible, down to earth advice not nonsense or fluff. Nevertheless, I sincerely apologise to the esteemed reader for not having enough time to write a shorter and more compact version of this article.

As usual, all my articles, if you have been following my blog, are works in progress: meaning they will be updated continuously. So please check always back for any new material. All the comments are welcomed although I mediate all the comments and do not publish any comment that is just praising me or praising this article or any comment that do not add much. Sorry! You can never imagine how much I hate praise and hate people who give praise. Please write a comment only if you have any tangible down to earth skills, techniques or advice that is germane to real/tangible PhD skills so we can all help struggling fellow candidates.

This article is an exposition of very essential general skills that spans a great number of PhDs. The article is more relevant to the PhDs in the natural sciences (Biology, Physics, Chemistry, Astronomy…), formal sciences (Mathematics, Theoretical Computer Science…), applied sciences (Engineering fields, Applied Computer Sciences…) and to social sciences (Sociology, Economics, Business and management, Anthropology, geography, Political Science, Psychology et cetera). Many skills such as mastering LaTeX might be considered less relevant to the folks doing research in the humanities. This article does NOT state specific skills but general essential ones that every PhD candidate should know or at least consider.  I do not write padding, fluff and non-sense similar to what you find in many books and articles. I am not the kind of guy that goes with the flow of the masses or with the whims of the flock. This article is your first true initiation into what a PhD really is and what you need to know to be prepared technically and emotionally. To be honest with you, ignoring what is written does not hurt me but hurts you. In either way, my knowledge of the human nature dictates that people need to baptised in  fire anyway no matter how much advice you give in advance. I think the ego plays a role here ( I will leave it to my friends who are studying Psychiatry & Psychology). General and essential in this article means also that you still need to learn specific skills and tools needed for your particular research field or for your particular research topic.

The pain that made me write this article, is that “no one waits for you” to acquire the skills needed and “no one cares about you” to put it that way. You will actually get blamed on why are you doing a PhD and you do not know X already? This is the hypocrisy itself! Skills mentioned here might not even be known by many academics. A lot of academics are good at giving the impression of knowledge veiled in a pile of academic bureaucracy and PR and not the real knowledge. Actually the most knowledgeable people I met in my field and I met many of them, are the most humble and the most down to earth, since as you know what the great Socrates said: “True knowledge exists in knowing that you know nothing“. Once the insignificant brains of many people, begin to grasp this phrase slowly (let this idea germinate in their superficial brains), and only then they might deserve the first level of respect. For the more research oriented readers, who are not convinced of Socrates wise statements (although that would be extremely stupid), you can look at what is called in Psychology: the Dunning-Kruger Effect, a good TED Ed video: (Why incompetent people think they’re amazing – David Dunning) I advise to watch for the lay person. This is why the majority of academics that I met who thought of themselves as very brilliant, were the most incompetent and of low quality in reality.

The following contemplation applies unfortunately to the current state of academia which glorifies empty wheat spikes:

“A wheat spike that is full of ripe grains, always bends its head down from the weight of its head; while the empty wheat spike is always arrogant with the head stiffed up, a head that contains nothing”.  Hussein Bakri

Real great men and women of knowledge bends down symbolically their heavy heads out of humility (i.e. they would not be able to raise them because they are very heavy & full symbolically speaking) similar to the wheat spikes full of ripe grains always bending their heads down – Image Credit

It is very tough to be in a hypocritical environment. I wrote this article NOT for the spoiled masses in the spoiled educational systems, but for the people who are disadvantaged because, out of their control, it happens that they did not choose the countries or regimes or religions or rotten ideologies or economical conditions or social conditions that were imposed on them. I am also writing this article, for those who are suffering and do not know where their first steps should be.

You can see why it is important to have an article like that. The skills in this article applies to all top universities in the west, not only to UK top universities, but my academic experience was gained from studying in the UK.

In many skills mentioned in this article it is pertinent to have an advanced level in the skill mentioned. Furthermore, and due to the academia being an extremely hypocritical environment, you are required to acquire those skills unfortunately quite fast almost miraculously! (remember no one waits for you!). I should mention with a strong tone that many of the skills presented in this article, are more emphasised in the natural sciences (Physics, Chemistry, Biology, Geology, Astronomy…), Mathematics, Computer Sciences vel cetera, than in other fields like the Humanities (Languages, Literature, History, Law, Philosophy, Divinity..). Social sciences (Economics, Politics, Psychology, Sociology etc…) which sits in between both camps, require from you to still master many of the skills mentioned in this article (as a concrete example statistics) especially if you are doing your PhD degree in a reputable UK university.

I read many non-sense PhD theses from well-respected UK universities including mine. PhD theses that were so boring and empty that I have slept 3 times while reading them. How did the author manage to write so much sugary language and fluff for the sake of the good Lord (If he exist), I have no clue?

I know there are many topics that are just silly and inconsequential; just non-sense to not use another word, ‘peanut butter’ kind of research; or fluff research that proves nothing but the obvious. People get the Dr. in front of their names for doing silly studies and fancy shmancy statistics; while the poor guy who is trying to improve the performance of the kernel of Linux operating system few additional nanoseconds or the poor girl who is trying to discover a cure for Cancer maybe gets nothing (negative results) simply because the tasks being investigated are extremely complicated. So if you are doing a PhD of the calibre that pertains to the category offluff peanut butter research, you do not have to master any of the skills in this article after all. Congratulations! go and have drinks and praise the Lord!

The ideal reader for this article could be someone who wants to do a PhD, or it could be someone who was accepted and waiting to start or a first year PhD candidate. It should be mentioned that there are a lot of books that cover the topic on how to do the PhD but in my opinion, the majority of them are too general, contain a lot of fluff  and they don’t give any tangible advice at all. This article provides down to earth advice coming from the experiences of many PhD candidates and supervisors. I will suggest at the end of the article four famous books on the topic of surviving the PhD and writing theses that I have read before I started my PhD in case you want to check them.

Please Note: this article is specific to the domain of PhDs/D.Phil (Doctorates of Philosophy in X). There are other doctoral qualifications not covered here such a doctorate by publication (a.k.a. by portfolio), practice-based doctorate and professional/industrial doctorate. That has been said, many skills and the majority of advice still apply to the other types of doctorates.

The following are SKILLS you are strongly advised to have (not specific to a domain or field):

Skills you MUST know if you plan on doing a PhD

DO NOT STEP INTO a PHD in a reputable university without knowing the following (Warning: mastering the following skills do not apply to “peanut butter” research in low quality universities):

PhD General Skills
PhD General Skills to know/do before the PhD

A – Master knowing how to choose the right and good supervisor/adviser & how to choose a good PhD topic

Important Notes before we start: This section covers one of the most important skills if not the most important skill of a PhD. The one which if discarded, leads to severe repercussions for the success of a PhD. How to choose a supervisor?! What is covered here pertains to traditional UK PhDs where in such PhDs, you would choose at least your primary supervisor. You are the one who usually approach an academic staff about a PhD topic. You both end up working on a PhD research proposal. The information here might be less relevant to industrial doctorates or other type of doctorates where your options are limited regarding the choice of a supervisor. In the UK system, there are two types of supervisors: primary and secondary. In the majority of cases, the primary supervisor is the most important in terms of control over the PhD supervision and the secondary supervisor role is more of a pastoral nature.  That has been said, I know in some universities and schools both could share supervision with a certain assigned percentage (60/40 or 55/45) or even equally. A friend of mine told me that during his PhD, the secondary supervisor was more important than the primary supervisor and this is because the secondary supervisor was a previous PhD supervisor of the first so the old dynamics were kept. Imagine! From my experience and the experience of may PhD candidates: it is always advisable to choose at least one supervisor who is very technical and could help you on the ground level of your research topic. In case you will have a primary supervisor who will be giving you  high-level advice, it is advisable to have a secondary supervisor who do the opposite & vice versa. Supervisors who give high-level advice does not mean giving you only language advice or  advice that lack research substance. You should not have such supervisors because they are useless – I will talk about this  later in this section.

The relationship in research degrees between a “supervisor” (a term used in some countries like UK) or an “adviser” and  a PhD candidate is more like an apprenticeship relationship than anything else meaning a relationship between an experienced research mentor” and a research “protogé”/”a mentee”. When embarking on a research journey, one would need an experienced individual to facilitate solving hurdles, to guide and to support. Unfortunately universities became complete financial “industries” and lost their initial purpose. This also affected the role of advisers which became more diluted and superseded by different roles such as what some supervisors consider themselves as  being more of “managers” with sticks and carrots not mere “mentors” (as it should be). A PhD adviser is a person  that you can easily change in theory the same way you change your shirt especially if that person is not useful anymore to your valuable time, to your valuable funding money (or the money of the tax payers), to your research or started to behave unethically which is not uncommon.

According to the UK Quality Assurance Agency for Higher Education doctorate guide [2] your supervisors acts as “expert guides and professional mentors as you develop both your research project and yourself as a research practitioner”.

Since you are working with them for 3 to 5 years, you need always to aim  for the [Good  AND (Boolean Operator) the Smart adviser/supervisor]. Supervisors are divided (unsurprisingly!) into four categories: [Stupid and Good],  [Stupid and Bad],  [Smart but extremely Egomaniac and Evil – Pure Evil] and [Good and Smart].

[Stupid and Good]  supervisors do not help you much in anything really and [Evil and Smart] is a big big trouble, the latter category tends to have supervisors that are extremely exploitative of their students and grumpy, moody, insecure and childish. Actually all the sets above in which you have the variable [Stupid] in them are detrimental to you and normally this category contains a lot lone wolves (academics who are not part of any research group or do not have a research group under their supervision). That has been said, there are a lot of lone wolves who are [Good and Smart] but the general tendency of this category is to have the Stupid Variable in my experience :-).

[Stupid Advisers/Supervisors] usually look at you with those stupid eyes and stupid faces powering the stupidity all over the floor, insignificant is their contribution to your learning and knowledge and technical advise! to the point you ask yourself: hmm..! what would be the difference, if I had my grandma as my PhD supervisor! Would anything change? Sometimes the only advise and feedback you get from these specimens is in the English language such as whether your spelling is right (I am not kidding!). It is not their job to look at the language of your reports or academic papers. That is not the kind of feedback you are waiting to get from an experienced research mentor. Their job is to give you “constructive feedback of substance to the matter” since this is why supposedly they are experienced researchers in the field in the first place. It does not harm if they do give you such language feedback in addition to the useful substance feedback especially in the writing up phase. In all UK Universities, there are normally complete institutions that help you with academic language. Per instance, in the University of St Andrews, there is the English Language Teaching  (ELT).

The truth of the matter is that a lot of such supervisors normally piggyback on other supervisors in the department whom are usually more intelligent. Some also piggyback on the work and intelligence of their PhD candidates or postdocs. This is in order for [Stupid Supervisors/Advisers] to survive academically (since we have the motto: “Publish or Perish“). They use the technique of: “Quid pro Quo” or “Scratch my back so I can scratch yours” especially in publications. They are the most pathetic specimens you can ever encounter and I wish you never encounter people like that. This is the type of UK supervisors/advisers that when things does not work as they should, shift the matter on you usually ignoring their incompetence and lack of skills.

[Smart Supervisors/Advisers] on the other hand, always always always know how to make things work. They know what actually works in the research field and what is worthy spending time on investigating  and what does not work and is a waste of time and money. Of course you have to do your own PhD, no one will do it for you, but they are the people that would spot a barren research path quite quickly and this is because they are extremely experienced. When you sit with them, you have your head full,  exploding with ideas in contrast when you sit with a moron, you have normally nothing. A hour with a [Smart Supervisor/Adviser] is worth 2 years of meetings with a [Stupid Adviser/Supervisor]. If you are a good, clever and hardworking student, you will always always always get a PhD with a Smart Supervisor no matter what unless something out of your control gets in your way.  Not to mention, [Smart Supervisors/Advisers] open usually a lot of doors for you in terms of helping you publish in conferences and journals, provide you with connections, normally they possess and procure many expensive equipment and resources you can take advantage of.  You should think of all what I am saying here when it comes to choosing a supervisor.

A scholarship and a monthly stipend is the money for the food on your table not for anything else, not for buying books or equipment or software licenses or Laptops. You need an adviser/supervisor that provides you with all the resources and equipment needed from funded research projects and that facilitates the access to people skills usually via a research group or connections to the industry especially for PhDs that requires that (scientific PhDs). Departments are hypocrite, they normally give you at the start of the PhD, a rotten old PC used by someone else and a desk and say hey! you are settled now! No you are not!

One thing to put always in mind, that if you have a scholarship even if you have obtained it with the blessings of an academic. That scholarship is NOT tied to that academic blessings and is NEVER GIVEN TO YOU BECAUSE OF HIM OR HER at least in well respected universities. It is given to you because you deserved it academically. It is a meritocratic system!!! I am saying that because a lot of people I know, who come from eastern cultures (i.e. Far East and Middle East) feel somehow they are indebted to the supervisor  and thus they remain with such supervisor even if they are not reaching anywhere. Move on!, a scholarship is the same as your own money (normally this money comes either from the EU, or from Companies, from foreign governments or from UK research councils meaning tax payers), so please take care of where you spend it and with whom you spend it. It is unfair to do otherwise, it is a hard earned money and the funder and the university are investing in you NOT in your supervisor/adviser. They do not really care so much about him or her. They do not give a damn! So please be at that level of responsibility!

A supervisor/adviser is nothing but a human resource for you. It is after all your PhD, your money, and your time taken from the most important years of your life. When people come to me  and tell me about problems with their supervisors having sever egomania or psychological insecurities or that he/she does not give them any descent feedback of substance or who ignores them for a long period of time. I told the individual usually to fire him/her (figuratively) and to quickly find another suitable and balanced academic. At the end of the day, it is your PhD not theirs and you will be blamed and you will be judged and examined at the end not them.

A quick fact: do you know that in the majority of UK Universities’ policies – hidden deep in the documentation that only someone like me might dig that up – (usually a lot of schools hide that from you because it is more work for them and involve some headache for them), If you happen to be in a situation where you need to change your primary supervisor, of course you need to have a very strong reason for that, the school can assign you a supervisor from outside the university if none in the department has the expertise  🙂  Yep!!! OUTSIDE YOUR DEPARTMENT!!!! so if there are no advisers other than this egomaniac in your department, you will not be screwed! this is just nonsense. How would be picking later your Viva external examiners in your field? just have one name less on that list of names that you might consider at the end of your PhD  and have him/her as a supervisor/adviser. Every problem has a solution, the most important thing is to work hard, move forward and get the PhD! The school, equipment, funding, the adviser etc… are only resources. Always remember this and don’t be convinced by anybody that it is something else! Pay attention: Some sicko supervisors like to sell you this idea that they have control over you, they don’t!

To reiterate, aim always to choose a supervisor/adviser who has considerable research money (In most cases, it means he/she is smart) and who has a complete research group with many PhD candidates and probably many Postdocs.  This means to you (to your benefit) that you will have access to a lot of resources, machines, equipment, expensive software etc… needed for your research and more importantly access to people skills and expertise. Also this opens opportunities for you to publish papers or to contribute to other people’s work in the form of collaboration. Pay attention in this category you have the other parameter to consider: Bad vs Good. If your luck happens to be terrible, and you lend on the [Bad] in this category, your life would be literally hell especially if it happens your luck was with those dubbed as the ‘untouchables‘.

Definition of untouchable academics: they are the academics with severe vitamin D deficiency who bring yearly millions of pounds worth of research money to your school to the point they become ‘untouchable gods‘ (or at least this is what they think initially). A lot of them have unresolved psychological complexes. The school has to keep them happy. So be careful with such a choice, always look for the [Good] Trait, because the [Bad] is literally lethal to your PhD. In addition, always aim for the supervisor who has an impeccable record of PhDs completions  in the funded normal duration (I will discuss this later).

Few horrible examples from experiences of friends and colleagues so be aware of what could happen to you. These experiences are authentic and happened in impeccable “high quality UK universities”. By the way, the “quality part” is an orchestrated PR thing only. I am stating all the incidents with the permission of the victims.

A very close friend of mine in a school not far at all from the School of Computer Science, in the University of St Andrews, tried to explicitly resign in an explicit audio recorded meeting from a company owned by his PhD supervisor. In other words, he happens to be a PhD candidate doing a PhD that is not industrial and he happens to work in a software company owned by his supervisor at the same time.  While on the topic: do not work in a company owned by your supervisor  even if you are tempted to do no matter what (assuming you are not doing an industrial PhD) – there will be definitely sooner or later a clear clash of interests. Not to mention this is will complicate matters if you would have to submit later an official stage 2 complaint to your university (since you would be considered a staff of the company (i.e. separate work policies that could be outside the University control) and a Ph.D candidate (internal policies of the university). In addition, employees have tribunal courts to take cases to.

My friend wanted to resign and stop working in the company so that he can focus solely in the remaining time and with the remaining funding money (tax payers money) left on his PhD. His job was to create silly websites for the company, a work not related at all to his PhD. The supervisor after hearing the resignation (i.e. the resignation was on the recording), have the audacity to force him to turn off the recording and then blackmailed him: He told him “Do your own bargain and I do my own bargain”, “I have a commitment to you so you have a commitment to me“. In other words, he is saying to the candidate “stay and work on crappy projects for my company  that are unrelated to your PhD while you run out of time and money in your PhD and I will keep supervising you i.e. helping you”. Well! it is not a “quid pro quo” or a “scratch my back so I can scratch yours” relationship.  The supervisor “bargain” is his job, he do not need a noble price for it and he MUST do it, whether in the case of my friend, he do not have to keep his “bargain” if his PhD is in jeopardy.  It is like dealing with a thug drug dealer. Supervisors who blackmail their students are academic thugs this is what they are!!! 

My friend used to tell me that whenever this cheap man used to be hungry like a boar, he came to my friend’s desk and tell him he is hungry explicitly, keep going back and forth with the same message and because my friend is from a very hospitable middle eastern culture, my friend used to buy his supervisor hundreds of Pizzas throughout 2 years. The amount of money spent just on pizzas is a lot more than the monthly salary he used to earn form this crappy company and the peanot butter silly projects he was working on. Not to mention the gifts this  man used to accept, my friend was trying to appease him and that is the problem actually, – you do not appease such behaviour – , you put an end to it soon! Not to mention, the extremely abusive language and behaviour that was used against my friend in terms of continuous use of the F word regularly in supposedly “professional meetings in a high quality university such as the University of St Andrews“, words such as “fuckhead“, “fucking“, “you are fucking agitated“,  even farting in one meeting without excuse  raising a little bit his “arse” from the chair (I will let you imagine that!) and without going to the toilet and telling my friend “your supervisor is talking from the arse” among many other examples that could not be said here (as if what I said is not hair-raising enough for the reader!!!).

This cheap specimen used to come and put his legs on my friend’s desk facing toward my friend, my friend boiling from inside, was under blackmail and fear of dropping his supervisor so he does not lose the PhD. My friend used to tell me even more horrible stories but for brevity I will not include them here. This same man (actually the university is the one with very low quality standards here for hiring such a man to deal with students in the first place), asked my friend to do a work of another PhD student that was published before without telling him explicitly, nor implicitly that it is actually is for another PhD student nor that it was published before knowing that one of the major & crucial requirements of a PhD is originality (i.e original contribution to the knowledge of the field).

This specimen – As you can see, I am intentionally avoiding using harsher adjectives here as much as I am tempted to but I ask the readers to choose the right adjectives for this supervisor’s low quality behaviour based on their moral compass and decency –  is known by the majority of people in the school as an extremely cheap and a low quality individual but is kept because unfortunately he and other similar academic specimens have long UK work contracts with weird laws and are rotting in the different schools of this supposedly high-ranked  high-quality university. The right place for such academics is on the street like the vermin they are.

This  married unprofessional man  allegedly (actually highly probable) has a pathetic “school boy” crush,  and probably more than that, on his female PhD student writing her academic papers/reports so that she can get undeserved funding and even asking his employees in his company and sometimes his PhD students (in a veiled and sneaky way), to do work that she would end up claiming credit for in a way or in another. Actually this girl told  my friend that the supervisor wrote her academic paper, the one that she submitted with her PhD application to gain acceptance and funding. As usual these things (any unorthodox unprofessional relationships) are difficult to be proven with evidence but not impossible. I mean it is difficult to prove a relationship such as that but one could easily prove  clear cases of favouritism resulting from relationships of this kind. There should be no favouritism of a PhD candidate vs another of the same adviser under any circumstances and for whatever reasons. Nothing wrong with love between two people (actually it is a beautiful thing) but this should NOT be at work, there are clear rules that govern that.

Always bare in mind that favouritism in all its forms and types is against most UK universities’ policies and against UK law. You might see favouritism  when a school spoils one PhD candidate with a lot of equipment and money while giving scraps to another (it is not uncommon at all). Any dating or unorthodox relationship (even with consent) between a supervisor/staff and the student if proven is a fire-able offence for both.  Supervisors/Advisers can NOT date their students without following specific procedures,  without ending the supervision relationship and without telling their management. This also applies to lecturers or  other staff members.

Another horrible example that was experienced by another friend of mine, I can only say that she is a lovely gal from New Zealand so that she can know that I talking about her here with her permission: her supervisor (a clearly pervert and deviant man) sexually assaulted her many times, initially he used to look always at her “boobs” -sorry for the expression – in supervisory meetings not her face like he is some kind of teenage pervert school boy. He made different approaches that she rejected vehemently on many occasions (until she complained about it explicitly) also he forced her to put the name of some academics in her department on her academic papers (they were like a thug cartel). These thugs have contributed  nothing to her work just so that he can get free academic papers back from them and their students. How pathetic is that!!!  This happened in United Kingdom (not a third world country), imagine! precisely in a very well-respected Scottish University. She complained and he was fired after a long investigation process (of course it is not in the University of St Andrews), it is in another Scottish University with a clear zero-tolerance policy against abuse, harassment of all kind especially the sexual kind, blackmail, exploitation and favouritism. I respect her and respect her strength and courage for coming forward and thank goodness that scum got what he deserves. You can never begin to imagine  the number of women who are victims yearly of such abuse and harassment in companies, public institutions and universities whom do not dare to do anything. It is really horrible!

Some universities unfortunately in the worst horrible offences (we are talking UK and US universities here), exercise  sanctions that affect the progress of the thug academic on the organisational ladder as was witnessed in some cases and this is a “not enough” behaviour from the part of supposedly high-quality reputable universities since a lot of cases merit and warrant strongly and clearly that the thug academic to be fired otherwise it will embolden other exploitative abusers, blackmailers and harassers.

I have been told by some academics that the culture of academia in UK is a culture of reputation so firing an academic does not destroy them. Acts they have committed destroys their reputation in their departments and throughout the university and other universities even if they stay in their jobs as shells. No one will hire them anywhere else. That is the real death sentence! It is more convenient that the reputation of such scum bag academics to be destroyed so that they reach a stage where they wish they were fired in the first place. They deserve to end up begging on the streets really.

If you have been wronged by a UK university or a staff member in a UK university (including supervisors), a lawsuit might be suitable option but follow first the normal path:  official complaint Stage 2 (a.k.a Level 2) + official investigation (do not forget to submit as much evidence as humanly possible (emails, audio recording etc.) -> if you are not happy with the resolution or there is evidence of a cover-up to protect the academic or the investigation has committed logical fallacies – one common fallacy is the straw man fallacy which involves either misrepresenting your argument and the evidence or to dilute them and then attack the diluted versions or to focus only on one component of your complaint with has the least concrete evidence while disregarding the more serious components with stronger evidence.

If you are dissatisfied with the outcome of your stage 2 complaint, you can  go to the governmental overseers of the Universities (their bosses if you want). In Scotland, there is the Scottish Public Services Ombudsman (SPSO). In England and Wales, there is the Office of the Independent Adjudicator (OIA). SPSO and OIA will investigate your case if you are not happy with the university’s outcome. The university has to give you an official written  outcome at the end of the investigation containing usually one of the following terms: Upheld (or Justified), Partially Upheld (or Partially Justified), Not Upheld (or Not Justified).  Make sure that the university will do that from the start of the complaint because OIA and SPSO needs to see an clear written outcome in order for their processes to start. In one case I heard of, the university intentionally did not give a clear outcome and kept blabbing to the student about how it can not do that under DPA (Data Protection Act) legislation. They knew that their staff member is extremely guilty and thus halted the process for many months so it did not go to OIA keeping their student in a horrible emotional condition reaching a breakdown. Imagine the kind of sneaky behaviour from universities! Eventually it went to OIA and they were forced to pay a big amount of money (5 zeros number) to the student as a compensation for all distress, extreme disrespect and inconvenience caused by such unprofessional behaviour and the university was forced to fire the staff member after a long process. This university is among the top 10 universities in UK so I am not bringing an example of universities which resides low on the ranking.

The OIA forced universities in many cases to pay  compensation reaching up to 400000£.  If you go along the path of OIA (England, Wales) or SPSO (Scotland), it is already so embarrassing  for the university  anyway since they aim usually to keep things without damaging their reputation in other words they aim to keep “their house in order“.

If SPSO or OIA folks per example, discovered any cover-up that the university is involved in (it has happened a lot before!), or discovered any disregard of any shred of evidence, any discrimination, any incompetence in the investigation process and or in the investigative team (like assigning the wrong guy or gal with the wrong expertise to be investigators,  bias  etc..) they are in lot of  trouble. Trust me! and their reputation will plummet to the bottom not to mention that this will give you a strong ground for a lawsuit that even SPSO/OIA folks will help you with.  If by that time the thug academic is not fired, the judge will force a termination of contract in addition to whatever the judge feels necessary in terms of damages and compensations for the suffering this has caused you emotionally, and the loss of big amount of time and money.  As if this is not enough, usually the media will pick up on the story very quickly  especially in a supposedly highly-reputable universities with supposedly highly moral compass and thus another death sentence! Justice is always served no matter the amount of time it takes for Lady Justice to exert her power and no matter how much conspirators try to stand against her!

I think there is a good reason to mention the following from cases I have been told about: a lot of international students mainly students from eastern cultures have a “weird sick and unhealthy dose of deference” to professors or doctors sometimes going to the level of being servile and docile to them instead of being critical and skeptical independent thinkers that should refuse requests that are detrimental to their research or requests that are out of the decorum. I heard a case of a PhD candidate doing the laundry for her supervisor in the University of Aberdeen 🙂 Come on, that is extremely sick! I do not know if it is just hearsay funny gossip or she is a friend with her supervisor and even friends do not do laundry for each others. International students mainly of Eastern background also think that if they argue with their supervisors that this is considered a rude gesture. A lot of students even avoid asking questions. This is due to the fact that generally in Eastern cultures, the elderly and the knowledgeable/wise is very revered almost deified.  This is not the case at all, you have to respectfully refuse or respectfully draw your red or even yellow lines in the sand and be very critical of everything including your supervisor’s character or behaviour.  I am using the word “respectfully” here to emphasise that you should always respect people to get back respect and also I do not want you to smash the face of your supervisor if he make a mistake even if he deserves it 🙂  Trust me there are many legal and policy-wise ways to punish bad behaviour coming from supervisors and staff to the point they wishe you have killed them instead.

Many filed misconduct cases show that this “weird behaviour of deference” from international students is easily exploited and taking advantage of, especially from bad and low quality UK supervisors. I think there is a moral duty for schools and universities to clarify that to international students. As an international Ph.D candidate, you do not have to have any unhealthy weird deference to anybody even to the principal of the University, just be normal that’s it! Call the principal by her first name only when you send her an email or see her!  The Ph.D aims to create out of the candidate a critical independent thinker. Check out the University of Glasgow video touching a little bit on the subject of “deference aspect” that differentiates international students from UK students. In this country (UK), you can be a professor with zillion published books and papers and still be called only by your first name without any pompous titles and that what the international students should know.

How to approach the matter of choosing a supervisor intelligently

What you are looking for here is two important sides of the prospective supervisor/adviser: the human/personality side and the technical expertise side. Both are equally important.

For the technical expertise side, it is quite easy to figure out if someone is suitable. You should read all his/her academic papers, all books, books’ chapters or at least most recent published work or most relevant work that you are interested in. Some academics have thousands of papers so it is not reasonable to spent months reading a prospective academic’s papers and at the end, he/she might not be interested.

PS: Please bear in mind that having a big number of published papers is not necessary an indication of intelligence or competence.

Some disciplines or fields have too much fluff and padding in their literature 🙂 while others don’t have that. In addition, an academic in medicine might have only 6 or 7 high quality peer-reviewed published papers and is considered an authority  in his/her field while another with 500 papers is not considered an authority. Number of papers do not mean anything, it is the quality of the academic papers, the quality of the journals where they are published in and more importantly than all of this is their impact that matters. Not to mention you see a lot of Piggybacking in academia or “scratch my back so I can scratch yours”  so a lot of academics end up on academic papers they have no clues what they contain. So be aware! Check also papers where the ” prospective supervisor is the first author”.

I advise you also to check the undergraduate/postgraduate courses/tutorials/labs/workshops that the prospective adviser/supervisor have taught in the past or is teaching now, and check also the presentations, the talks, the grants etc…

You might not have access to a lot of published papers  since you are outside academia. A lot of prospective supervisors are happy to send you a list of papers you are interested in (sometimes they send you final manuscripts so that they understandably avoid headaches with publishers), some upload manuscripts to ResearchGate or Academia.edu or you can ask a friend who has access to download the papers and then sent them to you. I am not expecting prospective students in poor third world countries to buy 5000 pounds worth of papers so they can make their research proposals. Expecting that is extremely hypocritical and ridiculous!

For the human/personality side: You should always aim to choose a good and well-balanced human being as a supervisor/adviser so to avoid all the headache you just read about in the previous section and please don’t say that this might not happen to you, it happened to a lot of intelligent, good and hard working students in highly reputable UK universities.

Now if you are outside academia, you would never know whether a supervisor is a descent balanced human being from just looking at his/her profile web page. Can you be sure that I am not a serial killer!? How would you be so sure! Well, let me clarify this fast, I am not! 🙂  The point I am driving home here, is that this type of knowledge is only gained on the ground and so your aim before investing 4 years+ of  the most expensive years of your life and before investing your money (whether self-funded or through a scholarship), is to seek advice from the people who have that kind of knowledge. By the way, a lot of idiots do not know that if you have a scholarship no matter who you are doing your PhD with, is still your own money. Funders are investing in you a lot of money not in the supervisor/adviser, they do not care if you do your PhD with Joe or Micheal or Monica. For them, they need the job to be done. So a scholarship is your own money that you deserved it not that of a supervisor/adviser (the only exception is if the money come directly from the supervisor own salary which is impossible).

(1) The first advice I can give is that you must ask academic staff with whom you have good relationship with, about the person you have in mind for a PhD supervision, what are the pros and cons of that person? The cons are more important that the fluff and the pros. Bare in mind, a lot of people are very very cautious to tell you negative things (which what you are after). Try to show them that you are trustworthy, that you are not talkative and that you are only asking so that you do not regret later. They will understand and start telling you the real truth about the person.

(2) Ask also PhD candidates about their advisers/supervisors but DO NOT ASK  FIRST or SECOND YEAR  PhD students (they are kissers of hands of their supervisors – so I do not use another word here). Ask LAST year PhD candidates who are just near submission or better, the people who have finished their PhDs with the adviser in question or even A LOT BETTER, people who have switched to go with other advisers/supervisors because of a problem since these people do not care anymore when they talk about the negative aspects of their previous supervisors. Finally, ask normally someone like me who does not give a damn about the world! and about the hypocrisy in the world!. Find someone like me in the school of your target.  It is impossible that you can find a person in the history of humanity, who do not have negative traits. I hate when I ask PhD students about their supervisors and the majority of them pathetically could not give me a single negative thing. This is unnatural, stupid, pathetic and hypocrite. Every person has flaws PERIOD. Everyone has a negative aspects of their personality, this why you should never ask the first and second year PhD candidate, they are too docile and afraid because you will get nothing. They are usually in desperate pathetic need of approval from their supervisors and are usually afraid of them and I think that is understandable.

(3) Do your own calculations of risks: How?

  • See (A) How many students have successfully finished their PhDs with the adviser in question?,
  • (B) How many have dropped or not finished and Why? (very important is the Why? more than anything else, with the why you can detect conflicts and their nature) and
  • (C) What was on average the duration of the PhD of this particular adviser? If long duration on average but with success at the end, it means the supervisor/adviser is either [EVIL and SMART] or his/her topics are just bananas. It is probable indication of Evil because probably s/he forcing his/her students to work on matters not related to their research.
  • (D) Look at the ethnic, gender and religious composition of his/her PhD students: If no Arabs, no Muslims, no people of different skin colour, no Jewish and the supervisor/adviser has been supervising for a long time, that means there is a strong probability of having either racism, or Anti- Muslims/Arab bias or Antisemitism. If there are no women between the students, and he is a man, that probably means that there might be a sexism issue or he is a religious fanatic!

There are a lot of indicators that could tell you a lot of things, all you need to do is to pay close attention! That’s it! All of this, if taken into consideration, will ease the PhD and make you find with more confidence that jackpot [Smart and Good] adviser that everyone is looking for.

Now one logical question, you might be asking yourself: how the hell I can know all this, if I am on my computer far away from the department or from the academic life? How the hell I would even investigate and ask? Well!! that is true, you cannot do it easily. This is why I always recommend if you want to do a PhD in a particular university /school, try to do a 1 year Taught Master degree first (if you can afford the time and money (either self funded or via a scholarship) and if you you have the will to do so). The Masters degree allows you to meet all the staff and to know them well enough. It allows you also to meet PhD candidates and Postdocs. Furthermore, the Masters is a good opportunity for you to find the right well-balanced and smart supervisor/adviser you are looking for to do a PhD with.

If you are heeding my advice in this regard, when you choose your Master dissertation topic, make sure the topic can be continued or expanded to a full PhD with the supervisor/adviser you have chosen after considerable deliberation or at least make sure that this is the research field you like. There are two main types of Masters in UK: Masters by Research (usually 2 years full-time) and Taught Masters (Usually 1 year full time). Masters by research are like  mini-PhDs, so you need a supervisor from day 1 which means you will fall with the same dilemma concerning the choice of a supervisor. On the other hand, in a Taught Masters, you have two semesters where you take many advanced modules and in the summer, you have to work on research project and to write a dissertation (duration: usually 4 months). In this time, you need to choose a supervisor and a topic. Taught Masters are ideal for meeting all your lecturers, and all PhD candidates in the department.

People usually encounter a big problem when they finish an undergraduate degree or even a taught Masters degree and then decide to do a PhD, especially when they did not plan well ahead the PhD itself while they were doing their undergraduate or Masters.  There is a huge gap to jump from a taught degree (such as the undergraduate or Taught Masters) to do a research degree. In addition, when you do a Masters and then go back to industry, you will find it extremely difficult to return back to do a PhD, mainly in terms of finding the right supervisor and in terms of coming out  “miraculously” with many research proposals. This is why I think when you do an undergraduate or taught Master, always think you will do a PhD even when you do not want to. You never know what the future holds! do you? In other words, make sure your undergraduate/ Masters degrees are initiatory for a PhD degree (both skills wise and supervisor wise) .

If you want to do a PhD and you are outside academia knowing with certitude that a PhD might span 4-5 years which means you have already accepted that huge penalty of time in your conscious and subconscious mind. In my opinion, adding an extra year of preparatory taught Masters won’t change anything and on the contrary will be very beneficial. The PhD is already a huge commitment in time, health, emotions, sanity and money (whether funded by a scholarship or not) and as I said before, it is not suitable for many people who have different aspirations in life.

If you do NOT want to commit a Masters, it might be up to your luck. At least find a way to ask about the supervisor a little bit before committing 4 years of your life. You can clearly know the technical abilities of your prospective supervisors/advisers from their academic papers, articles, books, presentations or courses taught etc.. but you will NEVER know their human side other than by the methods mentioned in this section or by similar methods.

Social media investigation: A lot of academics have social media accounts: Twitter, Facebook, LinkedIn etc… You have no idea how much information you can know in terms of personality from just looking at social media profiles of people.  I mean you probably heard that companies, electoral campaigns, governments etc.. are using social media to psychologically profile people on a massive scale via the use of Artificial Intelligence. A lot of people “leak” their personalities, their fears, their insecurities & their psychological complexes in their social media comments, tweets and posts. Per example, if you find the academic in question giving negative comments on Muslims even if they are veiled using indirect language that means a NO NO for you especially  if you are a Muslim  or if you find him/her giving bad comments against Jewish people or against any other category or minority that also means NO NO. Please pay attention to those things! I can study the ego of people from their social media posts on Facebook. Per example, if a person post their acceptance of a certain paper (supposedly in a high-quality conference) on Facebook, this inform me that he/she needs attention (ego issues), s/he cares about appearances so much and seeks grandeur as this is something that any good academic do anyway, in the sense of publishing tens of papers every year in high quality journals without the publicity! You got my point!

How to manage your supervisor/adviser + audio record all the meetings

Once you are in the PhD, you should be the one who manages your supervisor/adviser not the other way around. A supervisor/adviser is a human resource and like any resource need management. A supervisor/adviser has no power over you at all, he/she is just a human resource to help you finish your PhD. The adviser main job is to help you  gain a lot of research and transferable skills so that you will become an independent academic, a future adviser or a supervisor. Their duty is to give you constructive feedback of substance not English language feedback or silly feedback. You have of course to have the willingness to accept constructive criticism and to improve yourself. There are other components in the UK system that judges your progress and competence not the supervisor. They are mainly the yearly progress reviewers and Viva Voce examiners. In my university, reviewers can not be supervisors (primary or secondary) and can not be your previous supervisors. There are few exceptions to this. This is in order to make the system more transparent and objective. A yearly Ph.D review is usually a small Viva Voce. Please if you are not familiar with concepts and procedures of PhD assessment and Viva Voce examinations have a look at my well recommended article on the topic even at this stage (i.e. I am expecting the readers of this article to be prospective PhDs, or first year PhDs).

So it is your job not to allow UK supervisors to sell you the idea that you work for them for some reason or that they have any sort of power over you, they don’t in reality! A lot of them are psychologically sick with extremely bloated egos so pay attention and draw your lines in the sand from the beginning. Of course, the whole article talks about normal PhDs not the industrial ones where your supervisor/adviser might be your boss.

According to the majority of UK universities’ policies, a supervisor (which I believe a wrongly used term) has the job of an adviser + minor monitoring tasks that are not binding. The only thing a supervisor needs to “monitor you” is whether you and him/her have regular meetings. This is important for the university and important  for the UK Home Office folks if you are an international student.

On the PhD yearly review date, reviewers DO NOT have to take into consideration the report of the supervisor if they find that you have achieved a good progress. It is up to the reviewers to decide what is your actual progress really not to the supervisor. Usually reviewers take the opinion of the supervisor only on the ground that he or she have worked with you closely and know you better than them but many times they ignore the supervisor report especially if they find it biased and subjective which usually is the sign of an unhealthy relationship. You should make sure to raise issues when you have any problem (small or big) with supervisors first (give them a chance). Afterwards talk to the management of your school such as the Head of School (HoS) or the Director of Postgraduate Studies (DoPG) as soon as possible and even raise these problems in the yearly review session itself. The opinion of the supervisor is important but do not dictate anything and can be ignored easily in case of foul play or subjectivity.

Another thing I STRONGLY advise you to do is to document all your supervisory meetings. The best way to document what happens in those meetings is to audio record the supervisory meetings.

By doing this: you achieve first transparency, you can use recording for archival purposes and sometimes there are too much information and ideas exchanged in a meeting that warrant being recorded. It is good to re-listen to the meetings in case you missed anything. There should be nothing under the table and audio recording is the best mechanism to keep things transparent & professional. Many students I know used the recordings in official university complaints and legal cases against wrong doing of supervisors and their complaints were upheld because they have actual concrete submissable evidence.

Always organise your audio recordings into folders with date and time of the meeting. Inside the folder, in my case, I also have a Microsoft Word file containing the minutes of each meeting. You can of course, send those minutes after the meeting to the supervisor email in this way the supervisor can not tell you I told you this or that! Actually, I strongly advise you to do that. One of my friends in the school has a supervisor who is old I guess, the supervisor is a really nice guy but keeps forgetting stuff such as he always claims that he asked my friend to do something that he did not ask in reality.

Although you need consent for audio recording in the UK and the US (except if you are in the state of New York), any supervisor/advisor who refuses such consent -especially after you specified the reasons of recording supervisory meetings- is NOT GOOD FOR YOU. Again HE/SHE IS NOT GOOD FOR YOU!!! First sign you should change and flee running. Since if the main objective of the audio recordings is transparency (if you did something wrong or say something wrong, you will be held accountable – there is proof, same applies to her/him). There should be NO reason for not recording a supervisory meeting. Of course, you must tell them that the recordings are for personal use or for legal purposes in case things went south (this actually forces the insecure supervisor to come clean and keep his/her boundaries).  Actually you do not have to say even for legal purposes, because the law supersedes anything so it is implied that the university (the employer of the supervisor) or governmental bodies could use the recordings as they wish. In case, you were denied consent and you were obliged to keep the status quo, which you should not by the way – you will regret that!, keep sending the minutes of meetings to the email of the supervisor and in case you had a disagreement or he/she say or did something wrong  (harassment, blackmail, abuse, ask you to do tangential things not related to your PhD (very common), did not give you constructive feedback etc…), mention that in the email immediately after the meeting – do not be afraid at all (since this would constitute a strong evidence material for an official complaint) and would save you and your PhD if things went south. I come from a law family, so I know the motto “the law protects the weak but not the idiot“. So please do not be an idiot!

I have an article which goes into much detail of the forms of abuse, harassment, blackmail, exploitation, neglect, favouritism among other forms of misconduct committed by UK supervisors and academic staff which you can read here. The article provides techniques to protect yourself legally. It is still a protected article since many people are adding material form experiences. Also few of the folks I know that legally deal with  investigating cases submitted by PhD students are adding their advice to the article. Students do not know that they are in power and that they are protected and I blame universities for not giving them mandatory seminars explaining their rights.

I can say here a common blackmail is when a supervisor wants to end supervision because you disagreed academically with them or refused to do a tangential or irrelevant work. This is very common in many cases I have read. Students do not know that this is a form of blackmail that can put the supervisor in very serious trouble. If you did not do anything wrong yourself, he/she can not end the supervision without serious repercussions on their career. Blackmail is a very serious offence in UK which could  lead when indicted to a maximum of 14 years of imprisonment.  The most important  matter for any case is that you should  ALWAYS have strong concrete submissible evidence. So make sure you do that from the beginning. You never know! Emails and audio recordings are the best evidence.

Continuously ask the supervisor for feedback on your progress: The majority of sane people think that the supervisor is the one who should tell you how you are doing! or should warn you if your progress is not good and that is true and logical. If he/she does not say anything, for the majority of people with a high IQ, this means things are good. Since the system calls them “supervisors” after all (you see the wrong stupid semantics used in UK). In addition, the majority of people around the world consider silence to mean acceptance (everything is OK). A supervisor has to tell you and warn you when things are not working so that you have the chance to rectify in due time. It is unethical to do otherwise. Guess what a lot of UK supervisors don’t do that and then they pour it in the review report at the end of the year. This is literally gutless, cheap and unethical behaviour. We come from societies where we like people to look us in the eyes and tell us their opinions.  I strongly advise you, at least every month or two, to ask the supervisor clearly about your progress and have it recorded or mention it in the minutes that you send to the supervisor email. I used to ask my supervisor to give me a progress colour (Green, Yellow, Amber, Red) because our yearly review system gives PhD candidates “colours” as a way to grade progress in their PhD. This makes you aware of your progress and then it makes it clear for the reviewers that because you disagreed about X on date Y, the supervisor is saying your progress is not good while it was good before this “disagreement” (trust me! it happened with a guy in our school).

How to choose a good PhD topic that does not cause you serious mental health problems?

Justin Zobel lovely book titled “Writing for Computer Science” has a big part of a chapter that talks about how to choose a topic for research for a Master or PhD. You can find a lot of great ideas there. The ideas are NOT just for computer science and I believe they are applicable to any discipline. I am avoiding repeating them here but I will mention few notes. You usually approach an academic that is doing research in what you are interested in and then you would work with the academic in question (i.e. the prospective supervisor) on a research proposal.

My advise to you is to always always always choose a topic  that [first] is in tune with your capacities & then [second] is a topic you like or you are passionate about (I know this sometimes not easy since you might be tied to a topic for funding purposes) & then [third] is substantial and challenging enough to be considered at a PhD level.  Substantial contribution to knowledge is a major requirement for a PhD but substantial does not mean splitting the atom. 🙂 There are two essential requirements for a PhD here: the scope requirement  and the original contribution to knowledge of the field requirement.  Please read my article on PhD requirements, assessment and Viva Voce preparation to have an idea of what I am talking about here.

Pay attention: A topic that is revolutionary in a PhD is very very rare and in the absolute majority of cases where students choose such a stupid thing  end up literally failing. You can not split the atom 🙂  in PhD, please don’t do that for a PhD. Do it afterwards if you can 🙂 Well, you can of course ignore this advice, I can not prohibit you but in my experience this does not work and has never worked. After the PhD, it would be OK to choose a topic that leads to a Turing Award or Nobel Prize.

Ways in which ideas worthy of a PhD investigation are born?

  • Conversations with a lot of researchers in the field about an observation you have or a problem you heard about or read in a survey or about research gaps that are identified etc… This kind of brainstorming and conversations lead in many cases to interesting & worthy PhD topics. Usually either you have an idea or your prospective supervisor has one and you both end up discussing it and expanding it. You will normally develop a first draft of a research proposal which the supervisor can give you feedback on it and help you to hone it further before submitting it with an application.
  • Survey papers are amazing in summarising, contrasting and presenting research studies done in a certain topic. All descent surveys expose the challenges and problems that remain to be solved. These are great ideas to choose from. I mentioned different places where you can find published surveys in my article covering writing literature reviews.
  • Previous PhD theses of the school of your target are usually shared online and all PhD theses have further research sections with tons of ideas, it is good to contact the person and discuss those ideas. A lot of people are happy to engage in such conversations even if such person is not your prospective adviser.
  • … …

Now moving on with the skills…

B – Master Statistics

I know! That one course or 2 courses, you have taken in the undergraduate degree. Some students even do not take any statistics courses in their undergraduate especially educational systems that follow the American credits system where you can opt out easily of many courses. Statistics will surely hunt you in the PhD! My advice to you is: learn statistics well and I mean very very well!!! PS: There are some few fields and topics (especially in the humanities) where you might not need statistics.

First you MUST do a good refresher on Statistics (First and Second Level – 2 semesters courses as a minimum), then move on to MASTER hypothesis testing and statistical tests. You should understand the theoretical background of each test, the assumptions necessary for the test, what research scenarios (type of variables…) are ideal for the test, what research scenario in which you should avoid using the test:

  1. Tests of normality of a distribution: Kolmogorov-Smirnov test, Shapiro-Wilk test
  2. Correlation Tests: Pearson Test,  Pearson Partial Correlation, Point-Biserial Correlation, Spearman Correlation, Kendal’s tau-b, Goodman & Krouskall’s gamma, Somers d, Mantel Haenszel test of trend, Cochran Armitage trend test, Fisher Exact Test, Chi Square Test  and many others…
  3. Prediction Tests –  All Regression tests: Linear Regression, Standards and Hierarchical Multiple Regression, Binomial Logistic Regression, Ordinal Logistic Regression…
  4. One Sample Tests: One-sample t-test, Chi Square goodness of fit test…
  5. All types of ANOVA (Analysis of Variance) tests (One-Way, Two-Way, Three-Way… ), ANCOVA (Analysis of Covariance), MANOVA (Multivariate Analysis of Variance)….
  6.  Distribution-free tests such as Wilcoxon-Mann-Whitney test
  7. Useful non-parametric tests such as Mann-whitney U Test, Wald-Wolfowitz runs test, Wilcoxon signed ranks test, Kruskal-Wallis one-way analysis of variance by ranks test, Friedman’s two-way anaylysis of variance test …
  8. …. many many more…

Learn and understand how to draw the following plots, when to use them and what they actually show you:

  1. Plots that shows distribution:  histograms,  box plots a.k.a. (Box and whisker plots), Mosaic Plots, Violin Plots, Density plots, Ridgeline plot or Joyplot, Coplot (famous in Psychology)…
  2. Plots that shows correlations: scatter plots & scatter-plot matrix, line plots, heat maps (used so much), Correlograms, Bubbles plots, density plots …
  3. Plots that shows Ranking: Bar Plots, Spider Plots, histograms, Word Clouds, Stem and leaf plots…

Box plots are one of my favourite charts. They show you a lot of information about your data. They are very good at showing distribution but in a similar vein, they also show you outliers (meaning extreme values), the Upper adjacent value or in case of no outlier the maximum value, the Upper Quartile, the median, the lower quartile and the minimum in case no outliers or what is called as lower adjacent value.

The most important thing, I learned  is to never use a Pie Chart (see this article and this article). It is frown upon in academia especially in natural sciences and in Computer Science even in social sciences. Pie charts are for idiots, for stupid managers on the slides of a Monday morning presentation, so they can look at fancy colours and slices, and thus they can feel safe. Pie charts are used for public consumption like for a TV show. Pie charts should never be used in any descent scientific research paper.

Recommended Books to Read on Statistics:

  1. Discovering Statistics Using IBM SPSS Statistics Fifth Edition by Andy Field. This book teaches you statistical concepts and SPSS in the same time in a funny way (the author has a sense of humour). Another version of the same book that covers R: Discovering Statistics Using R. Don’t worry about R and SPSS, I will explain them in the next skill.
  2. Statistics in Plain English by Timothy Urdan
  3. Statistics by Hays.
  4. Head First Statistics by Dawn Griffiths – actually I am in love with the Head First series and the statistics book did not disappoint me.
  5. 100 Statistical Tests by Gopal Kanji – details of a lot of common statistical tests. Very easy to read and use when you need to know about a particular statistical test.
  6. Oxford Handbook of Medical Statistics by Janet and Philip Peacock – this book is very valuable guide covering a range of statistical techniques and definitions.
  7. Problem Solving: A Statistician’s Guide by Chris Chatfield.
  8. Applied Multi-variate data analysis by Everit and Dunn
  9. Multivariate Data Analysis by Hair and Anderson.

Recommended Books to read on plotting:

  1. The Visual Display of Quantitative Information by Edward R. Tufte (a classic that MUST be read by any PhD candidate if she/he is planning on drawing even a single plot) – this book is recommended by many PhD supervisors.  It is not important to visualise your data for the sake of visualising. You plots needs to be needed first of all. They need to be to the point and with minimum ink on the page 🙂

Statistics and Research Methods books

These category of books is very informative since you learn essential  descriptive and inferential statistics and in the same time you learn most important research methods in a certain field. There is another skill explained later in this article that emphasises on the need & pertinence of having a strong knowledge in the research process and in research methods  before embarking on a PhD and I strongly advise you to read dedicated books on research methods in your particular field before you start your PhD. For brevity, the following are few examples of books for  research methods + statistics needed in Pschology. You can find many similar books for many other fields.

  1. Research Methods and Statistics in Psychology by Hugh Coolican. This book covers both quantitative, qualitative and mixed methods research. In addition it covers a lot of statistics needed for Psychology research.
  2. Introduction to research methods and statistics in Psychology by Ronald McQueen and Christina Knussen – although this book is more targeted to undergraduate research than to postgraduate but nevertheless it is amazing, rich and easy to read (i.e. many research concepts are explained very well). It is also very useful  for an introductory level for research at a Master or PhD level. It is even very usefull for research outside Psychology.

Web resources

  1. The Statstutor website is a very good web resource for learning statistics. To get you started I advice you to read the Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests .
  2. The lovely University of Glasgow STEPS Statistics Glossary which contains a wealth of resources.
  3. If you are into free e-books and no money for hardcover/kindle books, I got you covered – Check out the CAST collection.
  4. WhatTest website is aimed to help out researchers and students to decide on what to choose as appropriate statistical analysis – Marvelous!!! Amazing guides and to the point!

Online Video Courses

Udemy platform

The following are some of the recommended online video courses to just get you started!

  1. Statistics for Business Analytics A-Z
  2. Become a Probability & Statistics Master
  3. Workshop in Probability and Statistics
  4. Statistics for Data Science and Business Analysis
  5. The Simplest & Easiest Course on Hypothesis Testing
  6. Math for Machine Learning (you know machine learning is nothing but statistical learning)- this course is more for Computer Science students interested into the Math of Machine learning

Interactive Multimedia Online Course

  1. Online Statistics Education – a very good multimedia course that teaches introductory statistics.

Suggested YouTube Channels and Courses:

  1. Crash Course Statistics: This course is amazing. It is a good crash course refresher on basic statistics. Actually all the CrashCourse YouTube channel is amazing. You should subscribe to it.
  2. MarinStatsLectures YouTube Channel: all sort of good statistics lectures. Just amazing! Please subscribe!
  3. statisticsfun YouTube Channel: all sort of good statistics lectures. Simply amazing! Please subscribe!
  4. BrunelASK Videos on YouTube – a collection of short videos covering statistics (including tests) in SPSS and Excel.

Side Section – Massive Online Video Courses Platforms

Before I proceed with the skills, I wanted to create a side section about online video courses’ platforms and their importance these days since I know a lot of people are not yet aware of them or of their pedagogical benefits. Not to mention, some of them are very democratic, meaning if you know a skill in demand you can create a course and begin to earn a lot of money. 2 instructors I know personally made from several video courses over £500000 in revenue (imagine!), it is simple maths –  some courses have hundreds of thousands of students enrolled in them from all around the world.

Why read books while there is a faster way? We are in the age of streaming massive online courses from platforms such as Udemy, Udacity, CourseraedX, Pimsleur, Khan Academy , Lynda.com , Codeacademy, Alison, Tuts+, Open Culture, OpenLearn, FutureLearn, Skillshare and pluralsight.com to your computer or to the mobile device in your pocket!! A lot of these platforms offer free online courses and even free nano-degrees/micro-degrees in many technologies, skills and disciplines. Many courses are given by professors from very reputable US & UK universities. In addition, you have also your good old YouTube and its numerous free educational channels!  iTunes U used to be something big but its light faded for some time now.

I lived all my life in a situation where  the only option for me is to read a complete book in order for me to acquire a particular skill or I had to read snippets of articles online that gave me incomplete and sometimes wrong way of learning something. That time fortunately is gone! Who has the time anyway to do that? I used also to be obsessed by pseudo sciences like Speed Reading and PhotoReading out of necessity of course (both do not work at all by the way – you think only have for a brief time the impression that they are working). I wanted a way to read faster so I can reduce the amount of time needed to acquire the particular skill needed for a project and then finally to be able to begin to work on the actual project!

The way these days you approach the matter. Suppose you want to learn a skill X, I would assume you are already enrolled in many of the platforms I have mentioned before.  If not go now and do it! You would then browse for a complete course or 2 courses -free or even paid- depending on your needs.

You take that course (spending 2 to 3 days watching a course). I like to watch the course (even the ones with 40 hours or 50 hours of video material) like I watch a Netflix or Amazon Prime series meaning not spending time doing stuff but getting over with the material and understanding it, this is because I am experienced now, I mean if I want to learn a new weird programming language, I already know many languages so I usually want to get a quick familiarity. This is my learning style, it does not suit all people. After taking the video course (spending 2 to 3 days max). I then begin straight away with the project at hand and if I need any further pointers or if I got stuck on an issue with no material online to cover it, I would ask questions on platforms such as stackoverflow  or similar places. In a PhD, in the majority of time, the folks in stackoverflow or similar places are clueless when it comes to helping you out with very complicated issues and software bugs so you even know better than most of them, this is not because you are intelligent 🙂  it is because you are working on something totally new.

It is pertinent to mention another good reason to enrol in platforms such as Coursera, Udacity and Udemy is the fact that all of these platforms have mobile applications on Android, Microsoft and iOS operating systems, which allow you to view their courses material while you are on the go (on the bus, on the train etc…). Many of these platforms also allow you to download the material to be viewed completely offline which is quite handy in the absence of an Internet connection.

Udemy Video Courses

Udemy is cool! Why? because with just 9.99$ or 9.99£ you can buy a 40 hours video course covering SPSS or R or on whatever topic you can imagine. You can buy a 70 hours course covering the Unity game engine!!! How cool is that? Do not buy any course when you see a higher price than 10$. Udemy is a bazaar market for courses, there is always a reason for Udemy to lower the prices to 10$ (St Patrick day, summer sale, Black Friday, Christmas, Fathers Day, Mothers day, New Students Sale… I mean it is quite funny…) not to mention that a lot of instructors can you discount coupons that would reduce the price of a particular online course to just 10$. I bought a very good and informative 20 hours video course for only 2$ by using a coupon provided by the instructor.

Udemy has a mobile app, where you can download the video lectures and watch them offline. The Udemy platform provides instructors with the ability to write quizzes and allow instructors to interact with students through Q&A forums among many other features.

Udemy is one of the most democratic platforms (I touched on this fact before), meaning you can become an instructor and teach a topic that you are proficient in and enjoy teaching while earning a lot of money (due to the sheer size of students from around the world that you have access to in case your course becomes successful)! You only need a very good microphone (to avoid noise), a laptop or PC and a screen recorder software like Camtasia which is very famous (or any free/open source equivalent) and voila! Some famous instructors, after they become rich :-), they even create a  complete studio with acoustic panels and lights setting.

The disadvantage of Udemy is that many times you might encounter terrible courses with terrible instructors. That’s what a bazaar or souk of courses is about! It is like those souks you go to in the middle and far east, where many times, you can actually buy a lot of good and valuable  stuff so cheap (Like buying a 500$ antique armoire with only 40$!  maybe the seller is desperate!), or you can be screwed by buying terrible courses and wasting money. Normally, courses that are best sellers are very good  in general. Always keep an eye on the reviews of courses, especially the negative ones. Generally, I always read negative reviews before buying anything online.

Monthly fee based video online platforms such as Skillshare and Pluralsight

Skillshare is like the Netflix for learning and online video courses. You pay only a monthly fee instead of buying each course alone, this provides at the time of writing access to tens of thousands of courses. Other similar learning services are Lynda.com, and pluralsight.com  but they are more oriented to Computer programming. You can find a lot of good material on these platforms. What differentiates Skillshare is that it is democratic like Udemy meaning  that you can find both bad courses and good courses. In addition, you can teach a course on Skillshare.
The beauty is in the fact that you pay only one fee which could be very low (it depends on what offer you get). You can pay as low as 7 sterling pounds a month with access to tens of thousands of courses (for readers from the future – this will probably change).

There are also many free courses if you do not want to pay a premium monthly fee but these are usually very short or very terrible. Skillshare has iOS and Android mobile apps so that you can learn on the go.

Bare in mind, there are a lot of topics not covered in Skillshare at the moment which you might find equivalent courses covered in other online video platforms such as Udemy, Coursera or Udacity but this is due to change in the future.

PS: some academic institutions have subscriptions to such online services especially to Linda so please check before you subscribe.

C – Master 1 or more Statistical Packages or Statistical Languages

This is a very important Skill needed for a PhD. Even if you are old school and you know all the math behind all the statistical tests. You need to master a Statistical Software or Package or a statistical and plotting Language since we do not live in the 60s.

You MUST know at least ONE of the following Statistical tools/ packages or languages stated in this article and you MUST master it. Now you ask which one? I can NOT give you a preference since that depends on the field, although I tried to specify the pros and cons of each software package or statistical language or plotting language.

As a good advice here, ask previous PhD students in your particular field and your particular topic even, what are the statistical packages or languages, they have used heavily in their research or ask academic staff that you know or ask your adviser. That has been said, I asked many  PhD students that I know throughout the years, from different fields (both in social sciences and in natural sciences) and it seems many of them either use R (top position) or SPSS (second position), or Python or MATLAB. These seems to be the most famous kids in the neighbourhood 🙂

Knowing MATLAB and knowing how to write scripts in it, seem to be extremely crucial with many folks doing research in many fields in Biology, Biotechnology, Neuroscience, Medicine,  Mathematics and Psychology (don’t ask why, I do not know but these results comes from my friends sample). MATLAB has a lot of tools out of the box for many fields and purposes like Maths and Statistics, Control Systems Design and Analysis, Signal processing and Communications, Image processing and Computer Vision, Computational finance, Computational biology, application development, database connectivity among many others. I wrote literally hundreds of lines of R code in my scripts throughout my PhD.

Realise Microsoft Excel is not on the list that will follow. Microsoft Excel is a powerful spreadsheet software but not quite suitable for a lot of cases where you need certain niche academic visualisations, or certain “statistical tests” (otherwise you have to do them manually if you know the Math or use what is available but why be like that? you can learn R or SPSS and it is a good investment).

Microsoft Excel plots are easy to do and I should say Microsoft Excel evolved to a stage where you can create very beautiful plots like heat maps and box plots but they do not give frankly a professional look especially in the context of sciences papers and theses. I can spot an Excel or similar software chart from 50 km away. Due to the enormous hypocrisy present in academia (does not apply to everybody of course), academics looks at Microsoft Excel plots or statistics as somehow inferior. I am telling you the truth, there is no point of telling you what you want to hear.

Nevertheless, Microsoft Excel with Pivot Tables and Visualisations might be an amazing skill to add to your skills set but not so much for a PhD sake, maybe if you are in some fields such as Finance or Management. But again PhD candidates in these fields, tend to prefer SPSS and R over using Microsoft Excel or in the best cases, they usually demote Excel to only very specific tasks like data cleaning (a.k.a data scrubbing) and preparing your data for SPSS or R or SAS.

Don’t get me wrong, I love Excel! I taught courses on Excel, I know every function and formula under the sun. I gave full day workshops on Excel. I taught writing Powerful Macros and VBA in Microsoft Excel and Microsoft Word. It is amazing what you can achieve with Excel! Actually, Excel has a funny dichotomy: It is in the same time, very simple and extremely powerful.

PS: Microsoft Excel can do few of the common statistical tests  and hey! maybe this is all what you might need for your research.

For a good Udemy course, I suggest the following:

  1. Statistics for Data Analysis Using Excel 2016 – It is a very good Udemy course, you would be amazed of what you can do in Excel (visualisations, descriptive statistics, t-test, chi square, ANOVA etc…)

Few online resources and published books:

  1. Saunders et al. Book on research methods in Business has a very good but very old Excel guides that can be found here. The statistics you learn still apply to newer versions of Excel.
  2. Excel Statistics: A Quick Guide by Neil Salkind.

If I am you, I would not bother reading books on Excel Statistics before the PhD, I would invest in learning doing statistics & plotting in a language (i.e scripting statistical language) such as R or using Python Data Science packages or SPSS or SAS or similar tools/languages that I will explain later. MS Excel is good only if you know for sure that you will be using very little statistics in your PhD.

The following section is an exposition on why it is important to choose to learn a Statistical & Plotting Language (which allows you to script) more than just learning to use a Statistical Software with a graphical user interface.

Important Notes on Scripting plots and Statistical Tests

Before we proceed with the list that shows statistics software or statistical languages or plotting languages that a PhD candidate should master,  I want to state the benefits of learning and mastering writing statistical and plotting scripts that you usually can do with statistical and plotting languages OVER using statistical software. When you write the statistical procedures and plots in a script (i.e. statistical code), you are able to “run” or “execute” the script whenever you want, wherever you want, and to produce with the same data-set, the plots or statistical tests that you need. This guarantees reproducibility which is one of the major tenets of doing science and research. In addition, scripting allows you to re-utilise your code for other scenarios that you might encounter later in your research.

Benefits of scripting (which are also benefits of statistical/plotting languages over other software packages):

(1) It creates a unified tidy appearance for all your plots (labels, axes..) in your papers and in your PhD Thesis and this is a plus.

(2) The most important aspect of sciences, including computer science is:  reproducibility. Scripting helps you tremendously in achieving reproducibility. Actually as a PhD candidate, you should keep all your underlying data with the statistical scripts you have created in a folder or some kind of organisation and set them aside to be re-used by yourself in another project, or by others in your research group and even to be submitted with your thesis or put online so that they can be available to others to replicate (on GitHub or Bitbucket or sites that share data-sets & statistical code). That is a big plus! Have a look at Zenodo also.  Zenodo is a research archive for datasets and for programming code (i.e. underpinning digital outputs of the research) that is purposed to make these digital outputs “citable” academically speaking. It gives you a DOI for your code repositories.

(3) Scripting is the recipe that tells you the story of how a plot came to be and how a statistical test was done. If you are reading this article before you start your PhD, I advice when you learn any scripting language specified in the next section, to create templates of all common plots and tests. Per example, an R template or a Python Template or a Gnuplot template for creating a box plot, or a matrix of box plots, or a heat plot, or a mosaic plot or an R script for doing an ANOVA test vel cetera.

(4) Screenshots of plots from many statistical software are unfortunately terrible to be put in a thesis or in an published paper or in a poster (usually they look pixelated or just weird). There is a simple technical explanation: Common Computer screens operate normally at 72 dpi (dot per inch) resolution, printed material can demand 600 dpi or even higher. The only way you can achieve such very good resolutions of images worthy of printing, is either through few statistics software packages (which can have such feature) or more commonly using statistical and plotting languages which all of them have such feature. R or Gnuplot per example, allow you to spit out (export) that kind of very high resolution PNG, JPEG or PDF files or whatever extension, that you need for your academic papers or your thesis. You can of course use scalar vector graphics plots or coded plots like TiKz and PGF for the LaTeX folks. In either way learn how to produce beautiful high resolution plots.

(5) The benefit of separation of data from appearance: Plotting languages such as Gnuplot (will be explained in detail later), and statistical and plotting languages like R and specialised Python packages have the advantage of separating the appearance (how the plot looks like) from the actual raw data and the cleaning process that was done on it.

(6) Statistical and Plotting Languages (ex: R, Gnuplot…) are by nature more powerful then Statistical Software (ex: SPSS) especially if they have a big community. Of course not necessarily easier. This is for a very clear reason that if there is a need for a plot x, or statistical test Y in statistics, there are definitely people who wrote a module or package to do that which you can import  easily in your scripts. This might not be available in Statistical Software especially the commercial  ones like SPSS where you need to wait for them to include a feature or a plot or statistical test that you might need right now. Something that might never happen even!

Important Notes on Plots in general

(1) Plots are essential instruments that show us what the data is actually saying from different perspectives and angles. They are tools not an aim in itself. It is very hard for you as a beginner to know what type of plots suits what type of scenario, what type of data and what type of relation you are trying to show between variables. I touched on that before, per example, use  only line charts when showing some sort of relationship between variables.

(2) Graphs will be always printed in white and black: So try to use a legend and a plot that is understandable to the reader when printed in white and black. I love using colours in graphs. If you are like me, make sure you differentiate lines in charts by dots, dashes, crosses etc…. I don’t always follow this advice myself.

(3) Consistency in labelling, axes, legends: Axes should start generally from 0  (there are some other cases where this is not needed). Labels and axes font and size should be legible and tidy.

SPSS (Statistical Package for the Social Sciences)

SPSS is extremely powerful software package and you are very advised to learn it and master it. Not only you should learn the tool but learn how to do every possible statistical test with this tool. Learn also how to draw all the plots mentioned in the previous section. Please do not be fooled by the word “Social” in it, it can be used by the natural sciences folks. A lot of PhD Candidates in Computer Science (my school) use it. What I am saying here, remove that hypocrite rotten component of your brain and learn and use SPSS if you need to.

SPSS can do amazing and powerful tasks with couple of clicks. When you are doing a PhD, you need every minute and hour so no need to spend writing complicated R  or Python scripts, if you can do what you want to do with three or four clicks in SPSS.

Advantages:

(1) Easy to use once you learn how to use the software, how to plot common charts and how to do statistical tests. The learning curve is easier than learning R Language (explained later) or Python statistical modules (Explained Later). This is why it is preferred more by social sciences folks.

(2) Powerful to a point: But hey! maybe that is exactly the power that you need for your research. Actually 80% of all research done  in scientific, social and humanities fields need just the power of what is available in SPSS. So you are safe! You can of course generate scripts in the weird language of SPSS so you can run your scripts again when you need to do the same statistics.

Disadvantages:

(1) Cost a lot of money (not free). You need to have a university or educational institution license to work with the software (Well!! that is contradictory to your situation now since you did not start your research degree yet).

(2) Not powerful enough. A lot of types of plots or advanced statistical tests are NOT available in SPSS but are available in languages such as R  (explained later) or Python statistical modules (Explained Later). In R, you can do machine learning, deep learning, advanced data analysis. It is an actual programming language which is extremely powerful.

A cool Web Site Resource for SPSS folks Called Laerd Statistics

Laerd Statistics: is a amazing website which contains literally everything you need to learn about statistical tests in SPSS (they have also material for STATA). The site contains the necessary assumptions for every test, everything really! Go have a check.  Even the web site contains HOW TO WRITE OR REPORT YOUR ACTUAL RESULTS in APA or other formats. what I mean here, the web site teaches you the right academic way to write your SPSS statistical Results. There is free material but the premium material is more complete. Don’t scratch your head when you read  the word premium. The 6 month subscription is  only few pounds ONLY (the price of coffee).

Furthermore, Laerd Statistics web site contains a Statistical Test Selector Tool (kind of an expert system) that tells you what statistical test you should use for a particular case. You give it the type of study and variables measurement types and it tells you what are the most suitable statistical tests to use. I mean if this is not awesome and cool? I do not know what is! Unfortunately as usual, I don’t have good luck usually (hope this changes) so I discovered the web site almost at the end of my PhD. I wished someone told me about this web site 4 years ago!!!!

Recommended Books

When I recommend Books I don’t just  throw at you some fluff books that a lot of people suggest. The ones that require a century for you to begin to grasp what you need. I hate the books that I need to read a 100 pages before I begin to read the meaty part. A lot of authors just like padding. I recommend normally books that are enjoyable to read and lead you somewhere after reading them. Books that have tangible effect. I read many books that wasted my time before.

The easiest best book for Learning Statistics and then applying it using SPSS (Top ONE) is:

  1. Discovering Statistics Using IBM SPSS Statistics Fifth Edition by Andy Field.

Pay attention please, the above book is not an SPSS cookbook. It teaches statistics as a main focus with applications in SPSS. You have many books that teaches only SPSS to an advanced level.

Actually, you have two types of books. First Types: Actual Statistics books + maybe a limited explanation of some software or language to apply what you have learned. The second type involves Statistical Software Cookbooks. You normally buy books from the second category only when you are already master Statistics in general not the other way around like a lot of people do who end up nowhere.

Other good SPSS books:

  1. SPSS Survival Manual: A Step by Step Guide to Data Analysis using IBM SPSS by Pallant.
  2. Quantitative Analysis and IBM® SPSS® Statistics: A Guide for Business and Finance by Abdulkader Aljandali

Some books are for certain fields such as Psychology. I recommend the following list of books based on recommendations of a friend of mine who is doing a PhD in Psychology :

  1. Statistics Without Maths for Psychology by Prof Christine Dancey and John Reidy
  2. Making Sense of Statistics in Psychology – A second Level Course by Brian S. Everitt. This book although old but amazing. I even read couples of chapters from it and learned a lot.
  3. An Introduction to Statistics in Psychology  by Dennis Howitt and Duncan Cramer. Waw! if you are doing Psychology, I envy you for the existence of this book!!! Everything you need to know from descriptive statistics, significance testing, t-tests, chi square, Analysis of Variance and many many more. I read the first 2 parts of it and I will tell you this: the book is great even for us in Computer Science (Don’t forget that computer scientists do a lot of experiments that involve human subjects) .
  4. Statistical Methods for Psychology by David Howell.

SPSS Cookbooks Just for Psychology:

  1. SPSS for Psychologists by Nicolas Brace et al.

Suggestion of Good Udemy Video Courses for SPSS (some of them are recipe courses not purely statistics courses):

  1. Statistics/Data Analysis with SPSS: Descriptive Statistics
  2. Statistics / Data Analysis in SPSS: Inferential Statistics
  3. SPSS For Research
  4. SPSS Masterclass: Learn SPSS From Scratch to Advanced
  5. Statistics Made Easy by Example for Analytics/ data science

Stata Software Package

…to be expanded later…

SAS (Statistical Analysis System)

SAS is both a statistical software and a statistical & plotting programming language that is extremely powerful. The software is NOT free but there is an educational version suitable for students. You can with SAS (as software and as a language) handle very large datasets, scrub your data, plot it with different visualisations, do statistical tests and techniques and much much more. SAS is almost equivalent to R in terms of being a good choice for you in your PhD. I said ‘almost‘ since R is a lot more powerful especially when it comes to machine/deep learning and advanced ‘edge statistics’.

R is the open source equivalent if you want of SAS. Another analogy if I am allowed to make, R is like Linux OS and SAS is like MS Windows. SAS is easy to use (compared to R of course), but little bit less powerful.

On one hand, SAS as a language, in my opinion, is intuitive and easy to understand. I find that when I wrote few scripts in SAS and compared that with same procedures I wrote in the beginning of my PhD in R. From my experience, R scripts can become very messy, very fast. On the other hand, R can do stuff that SAS can never do (especially aspects of predictive models, machine learning and deep learning). R also (due to its free and open source nature), has always packages with the latest statistical techniques, something you might not find in SAS. That has been said, you do not have to worry about that ‘edge statistics’  you find in R if you do not need it in your research.  But to be fair, R is more valuable to you in my opinion, if you want to learn one or the other.

Tableau Software

Tableau allows you to create very beautiful interactive and non-interactive visualisations of your data. It is used mainly in the domains of Business Intelligence and Business Analytics but recently expanded to more research oriented domains. Bare in mind there are many differences that governs the semantics of both the term ‘Analysis‘ (dealing with past data, decomposing it and examining it and extracting meaning out of of data) and ‘Analytics  (dealing with the future: with predictions based on Analysis of past data).

Minitab Statistics Package

Minitab is a commercial statistics package. This is loved by the Psychology folks in particular and by social sciences in general.

——–For More Power———

Go and learn a statistical languages. All the software packages mentioned above can NOT DO many things that the statistical Languages and general purpose languages can do. I will come back to this part of the article later and mention what R can do that SPSS can NOT do in terms of Statistics.

R Statistical Language

R is the king of all statistical automation. It is statistical & plotting programming language. It is a free and open source. R can do stuff that SPSS/SAS can never do. But that comes with some expense: a learning curve.

If you decide to go with learning R. You should not only learn the language but you should also learn how to do every possible statistical test with it. In addition, learn also how to draw all the plots mentioned in the first section.

One of the most famous software/IDE used in R is RStudio. You have of course to install R first.

—to be expanded further — I will expand this section when I have more time to talk about famous packages in R that a PhD candidate should be familiar with and what these packages do: ggplot… I will talk about one of the biggest advantage of R is which the fact that it can integrate nicely through packages like Tikz and xtable with LaTeX ….

For the inpatient and avid reader, please refer to my articles of how to integrate R  plots and statistical tables into LaTeX and how to integrate LaTeX into R itself (Articles 1, 23 and 4).

Recommended Books

Bear in mind that these are cookbooks or books that teach R only. These books do not teach  statistics. You need to master Statistics before (See Skill A).

Of course you will definitely find yourself in need of using the ggplot2 R package so a classical and very good book covering the topic is:

For a book that teaches both Statistics and R (an awesome, enjoyable, funny and easy to read book!):

  • Discovering Statistics Using R by Andy Field.  The author is quite funny and you will enjoy and begin to like statistics (if you hated this topic in the past).

Web resources

  1. The R journal which is the official open source reviewed journal for R statistical language
  2. The rprogramming.net awesome website
  3. The R documentation and manuals website

Online video courses

Since we do not live in the stone ages anymore, as I said the fastest way to learn is through a video course.

Udemy

PS: Don’t buy courses when their prices are more than 10$ (the price of a lunch). As I said before Udemy is a bazaar market for courses, they always have sales so prices of courses will be reduced to 10$. Plus a lot of instructors can give you vouchers or they usually offer discounts when you buy one of their courses. (Bazaar does not necessary mean bad, it means cheap, but yeh! some courses are really bad so avoid spending your money on them. Buy always best seller courses with thousands or hundreds of thousands of students or the highly ranked ones).

Old S and S-Plus Statistical Languages

No one uses S Statistical Language since the open source GNU free implementation/alternative of the S language is nothing but the R language (stated above). But S-Plus is a commercial implementation of S programming language and is used by some of the folks from social sciences.

If you want my opinion, learn R and that it! You own the world!

MATLAB (Cost Money) or GNU Octave (Free)

MATLAB is one of the giants. It is cost good amount of money for sure, so a lot of the folks I know learn MATLAB and its mathematical and plotting programming language either via books, videos resources or video course , traditional courses, lectures etc…and then apply the knowledge (same results) in GNU Octave. GNU Octave is a software that provides the MATLAB “Scientific Programming Language”. The language  is based on “powerful mathematics-oriented syntax with built-in plotting and visualisation tools “. A lot of academic institutions like Universities have normally MATLAB Licenses but for the folks out there who are disadvantaged (remember this article is for their sake), I advice them to download and install GNU Octave and then use the software as you use MATLAB.

Python Statistical and Plotting Modules

It is a good idea for you to learn the Python programming language even if you are not a Computer Scientist or a programmer. In addition, you do not need to be a data scientist to follow the Python path. Python is an easy language to learn. This section covers the following important Python modules for data analysis and visualisation: Numpy, Scipy, Pandas, StatsModelsSeaborn and matplotlib. That does not mean that these are the only ones used. But it is fair to say the aforementioned Python modules are the most famous and most used by PhD candidates. Some add to the list scikit-learn for “statistical learning” a.k.a by the buzz word: “machine learning”.

A side note: I strongly advise you to register on Kaggle, an online community of data scientists and machine learners. Kaggle allow you to access tons of learning material covering Python, Machine learning, deep learning, Pandas, R etc…). It hosts also a huge number of data sets ideal for inferential statistics and machine learning. Another cool thing is the  competitions with thousands of dollars as rewards that you can participate in and earn a lot of money.

All the following modules are under the banner of “Data Analysis and Visualisation in Python”. It is better to learn them as part of an overarching course or topic instead of getting bogged down with one module or another. Since, in my experience, you will need to use them in tandem for many  cases. Per instance, you might use in the same Python script, Pandas to import an Excel (.xlsx) file, Numpy to do some scrubbing and manipulation of your data, seaborn and matplotlib to plot your data etc…

I advise you when you learn these modules to create many templates to be used later. Per example, a template of how to import data  (when the header is available or not) from an Excel workbook, another for importing from a CSV (Comma Separated Values) files,  or from a Tab delimited files,  or from a MySQL database vel cetera.  Similarly, create templates for different types of plots and statistical tests. This is will make it easier for you to use them later for different research projects.

Numpy and SciPy

Numpy is a package that supplements your data analysis tool set with advanced data structures such as multidimensional arrays and matrices helping you organise and process large data.

Scipy is an eco-system really not one library or a module which contain under its banner other libraries such as Numpy, Pandas and Matplotlib, among others. It is the “scientific python” in itself as its name suggests. It covers whatever you can imagine doing in mathematics, statistics, scientific calculations and engineering.

Per instance, you can use the Scipy stack of tools or eco-system to do a one way ANOVA test via the (f_oneway() method) but people usually use a dedicated library for statistical tests called StatsModels which I will discuss later.

Pandas

Pandas is a famous Python library for data analysis. The library also provides advanced data structures. It also contains some visualisations although it is not its main objective to do visualisations. Actually, it is better to use matplotlib and seaborn for visualisations.

StatsModels

StatsModels is your module for statistics and statistical tests. Name any statistical test and you will probably find it in this module.

The following two sections cover few things about 2 awesome and important visualisation modules: matplotlib and seaborn. My advise to you is to learn them both. Start with matplotlib module and then move on to learn the seaborn module. Why? well! matplotlib allows you to draw many types of plots that are basic but seaborn have extra types of plots (like faceting plots) and things of that sort you might find quite handy.

Matplotlib

Matplotlib is a famous 2D plotting library in Python. The types of plots you can draw with the Matplolib library are just awesome. Please have a look at the examples in this link and you will be astonished. Actually, I regret I did not use this Python library instead of R from the beginning of my PhD. Now usually you need some knowledge also in Numpy and Pandas Python modules while working with matplotlib (so all these 3 modules supplement each others in a way). Per Example, I use the  Pandas module to clean up raw data and import it from different sources (Comma Separated Values (.CSV) files, Excel, Tab delimited files vel cetera).

Why matplotlib module is awesome and powerful?

I could write a complete book on this but I just want to mention in this section few advantages I have found. First, writing my plots scripts in Python as a language (compared to R per example) is very advantageous. As you know Python is an easy language that makes code extremely readable (one of the main selling points of Python). Second, the concept of matplotlib ‘styles‘ or ‘styles sheets’ is just amazing. matplotlib has styles that make your graphs look like a ggplot R plot (i.e. emulating R ggplot), or like plots of seaborn module, among many other styles. There is a great number of styles you can choose from. You can also create your own style. So you can specify how the axes should look like, the colours, sizes, labels, the ticks etc.. and then apply the style to your graphs without redoing the work.  Third, matplotlib is amazing in creating grids of plots and subplots (meaning several plots in a grid in one figure), it is  a lot easier to do that than using R. In addition, matplotlib makes it relatively easy to make plots that share axes or to create many axes or dimensions in your graph (trust me you will need this at some point) and most of all matplotlib is awesome in creating something called ‘inset plot‘. One cool type of inset plot is called “Zoomed (in/out) Inset plot” (please see next section).

Awesome plots I was able to make with Matplotlib

There is a graph that I saw once in a paper and I was fascinated by it. I was also very frustrated because I wanted also to be able to do something like it for a scenario that I needed. The paper was published in 2003 if my memory does not betray me. I reached a stage I wanted  to contact the authors (of course that would have been extremely silly)

How the hell did these folks do that?
How the hell did these folks do that?

Finally an eureka moment arrived and I discovered this type of plot is called “Zoomed-Out Inset Plot“. R has the ability to create inset plots but positioning them is just weird and there is no zooming effect as can be seen in the image above. The only place where I managed to replicate exactly that plot is with Python Matplotlib. Thus finally I could sleep at night since I knew the secrets of these authors (they used Python Matplotlib). Check this person blog , or the Inset Locator Demo2 on Matplotlib documentation, if you would like to know how to create them. If you know other systems that can do that, please leave that in the comments.

On many occasions, you draw a plot on a scale to show the overall trend of the lines but sometimes you have a part of the graph which is either too jammed or too small. The “Zoomed-Out or Zoomed In Inset plots” are ideal for such cases relieving from you the burden of drawing two or more plots.

Matplotlib and LaTeX

In matplotlib you can save your plots in different formats as follows:

import matplotlib.pyplot as plt
... code of your plot...
plt.savefig('images/creatingstyles.png')
plt.savefig('images/creatingstyles.pdf')
plt.savefig('images/creatingstyles2.png', dpi=600)
#if needed
plt.show()

In the above example, I saved my figure as a PNG, as a PDF and as a PNG while specifying the resolution as Dot Per Inch (dpi). There is always this annoying problem of generating figures worthy of publication quality especially for academic papers, theses and for printed posters. You will always feel frustrated by the quality of the images that you normally generate which normally are ugly and pixelated (especially in the case of a poster, per instance). The DPI parameter might provide some relief for cases where you use your images in MS Word/MS PowerPoint. But If you use LaTeX there are other better techniques.

LaTeX into matplotlib plots and vice versa

There are many techniques to make matplotlib integrate well with LaTeX. I would assume LaTeX is installed of course.

First technique: startup style configuration RC Params

This technique is somehow similar to the next one but done differently. You can include any LaTeX code (both math and text modes) inside any matplotlib text feature (like labels of Axis, titles, annotations vel cetera). It is better explained in the following example:

#!/usr/bin/python3
import matplotlib.pyplot as plt

test_scores = [34, 65, 76, 88, 98, 96, 32, 65, 34, 87, 78, 67, 89, 90, 100, 87, 66, 69, 72, 100]
time_spent = [11, 23, 34, 32, 33, 45, 21, 50, 12, 60, 56, 53, 50, 44, 50, 56, 40, 34, 67, 60]

plt.rc('text', usetex=True)
plt.scatter(time_spent, test_scores)

plt.title(r'Test scores VS Time Spent \n this is a LaTeX formula:$\Delta x = y^2

The figure generated will look like the following:

LaTeX code inside matplotlib chart

As you can see from the example code and figure above, we managed to write LaTeX commands inside a matplotlib plot (e.g. \textbf{ } even using the math mode $…$ to include symbols and equations). You can see how much this can be convenient.

Same outcome can be achieved by specifying  also matplotlib.rcParams[‘text.usetex’] =true.

Second Technique: producing plots as PS file (Postscript)

You need to specify your parameters into a dictionary for the rcParams. You should make sure that the ‘backend’ option is ‘ps’ and the ‘text.usetex’ should be True .  So you specify the parameters as you want in the following form (example taken from LaTeXify Matplotlib, Matplotlib: latex examples)

fig_wdith_pt = 246 #this can be obtained from LaTeX by using \showthe\columnwidth
#or from your academic paper LaTeX style template
#(so you need the width of the column (if 2 column or 1 column)
#could be generalised to Poster
inches_per_pt =1.0/72.27  #an inch is 72.27 points
golden_mean = (sqrt(5) - 1.0)/2.0 #Aesthetic ratio
fig_width = inches_per_pt * fig_wdith_pt  #Get you the width in inches
fig_height = golden_mean * fig_width #Get you the height in inches, preserving the golden ratio mean

params = {'backend': 'ps',
          'text.latex.preamble': ['\usepackage{gensymb}'],
          'axes.labelsize': 8, # fontsize for x and y labels (was 10)
          'axes.titlesize': 8,
          'text.fontsize': 8, # was 10
          'legend.fontsize': 8, # was 10
          'xtick.labelsize': 8,
          'ytick.labelsize': 8,
          'text.usetex': True,
          'figure.figsize': [fig_width,fig_height],
          'font.family': 'serif'
    }
matplotlib.rcParams.update(params)
...code for the plot...
plt.savefig('image.eps')

Then the encapsulated Postscript Vector Graphics file (.eps) can be included into you LaTeX like any other image. To tell you the truth, there is even a better approach with a better quality which is to transform your plot into a plotting language used quite often in LaTeX, known as TiKz. So in other words, it is like LaTeX itself is plotting the matplotlib chart. How to do that? please see next section.

Transforming matplotlib graphs to LaTeX TiKZ

There is a good library that can be found here that coverts a matplotlib figure into Tikz/PGFplots  LaTeX code which you can then be integrated into your LaTeX document (On LaTeX side, you can do the same procedure that I have explained in my series 1, 2,  covering R and LaTeX)

Resources

The following resources are taken from the matplotlib folks.

Books, Chapters and Articles

Videos

Tutorials

Seaborn

Seaborn is an exciting data visualisation library. Seaborn can let do things similar to what ggplot2 package in R can let you do. The example gallery of some of the plots that Seaborn can draw, are  found here. I like the fact that you can add many ‘semantics‘ to any plot you can imagine; sort of dimensions or additional variables to your plot. You can supplement any plot with semantics like “hue” (meaning you add another variable to the plot shown in different colours), “size” ( the variable is shown by size) and “style” (an additional variable or dimension is drawn with different styles of lines or shapes of bins etc…).

Seaborn is based on matplotlib  so you might need also to be familiar with matplotlib. Actually, as I mentioned before, learn data visualisation and analysis in Python as “a whole” not just particular Python modules. It will then be easier for you to learn, if you need, the specifics of a Python Module.

ggplot2 is very famous in R in doing facet grids or faceting (i.e. plotting many variables both categorical and numerical showing many dimensions and angles). Seaborn allows you to do many variations of that in Python. You can use two parameters (row=X , column=Y) for any plot function under the Sun in Seaborn. These will facet the plot into rows/columns of plots based on the dimensions in X variable or Y variable. That is really awesome I believe. I found that the Python Seaborn module is a lot easier than using the R ggplot2 package.

Gnuplot Tool

This tool is loved by the old folks in my school. This tool is ONLY a plotting tool that creates awesome and very beautiful statistical plots. It works by writing your plot in a specific plotting language. The tool also integrates very well with LaTeX (actually I should say extremely well). If you are a computer scientist, I should tell you that this tool and its language integrate very well in Linux shell scripts or Windows Powershell or anything similar.

Gnuplot Power

For a demo of what gnuplot can draw, please have a look at the gnuplot 5 demo plots

The journey from Gnuplot to LaTeX

All the following methods explains what to do if you have a plot you already scripted in gnuplot and you want to include it into your LaTeX document.

The latex GNUplot terminal for LaTeX

This is one of the most famous terminal to output to if you want to output a picture environment.

set terminal latex
set output ‘plot.tex’
set title ‘Awesome Plot’
set xlabel ‘X Axis Label’
set ylabel ‘Y Axis Label’
plot (x/4)**2, sin(x), 1/x

This will produce a plot.tex file per example containing the LaTeX picture environment which you can include into LaTeX document.

% GNUPLOT: LaTeX picture
\setlength{\unitlength}{0.240900pt}
\ifx\plotpoint\undefined\newsavebox{\plotpoint}\fi
\begin{picture}(1500,900)(0,0)
\sbox{\plotpoint}{\rule[-0.200pt]{0.400pt}{0.400pt}}%
\put(130.0,82.0){\rule[-0.200pt]{4.818pt}{0.400pt}}
….
….
\end{picture}

You can later include the .tex wherever you want in the document in the form:

\begin{figure}[h!]
\centering
\input{plot2}
\caption[Short Caption Goes Here]{Long Caption Goes Here}
\label{fig:AwsomeGNUplot}
\end{figure}

The only difference here is instead of putting \includegraphics[width=0.5\textwidth]{figure} you put \input{…} with the name of the plot without the .tex

The epslatex GNUplot terminal for LaTeX

set terminal epslatex

It is LaTeX picture environment using graphicx package and produces a Postscript file and .tex file. In other sense, this technique produces a LaTeX picture with Postscript. You set the output as a .tex file. Same technique exactly as above.

The emtex GNUplot terminal for LateX

set terminal emtex

LaTeX picture environment with emTeX specials

The TikZ terminal for LaTeX

set terminal tikz

A terminal that allows you to output your plot as LaTeX TikZ graphics macro via the lua script driver (which still export plots files as .tex files)  thus you then include those plots inside your LaTeX Document with the technique mentioned above.

The texdraw terminal for LaTeX

set terminal texdraw

A terminal that allows you to output your plot as LaTeX texdraw environment.

The pstricks GNUplot terminal

A terminal that allows you to output your plot as LaTeX picture environment with PSTricks macros.

Gnuplot code Inside LaTeX

Do you know that you do not have to switch to gnuplot as an external program to generate your .tex plots? You can embed your gnuplot plotting code, from inside your LaTeX document itself. Awesome isn’t it? This can be done through a LaTeX package called  gnuplottex which allows you to include/embed Gnu­plot code inside your LaTeX documents.

D – Master using bibliography management systems such as Mendeley or Endnote

If you are doing things manually in terms of bibliography inside MS Word: per example writing each reference manually letter by letter, stop it! this is too primitive, too manual, too pre-computers era and a complete waste of time. You need to use a bibliography management software or tool.

Master a bibliography management software from the names mentioned in the next paragraph. Know that all of them have plugins for Microsoft Word and other word processors.

There is Mendeleya very good free tool that I used before moving to LaTeX/BibTeX/BibLaTeX, it has neat features and it is good for the MS Word folks out there. There is also Endnote and some Web Browser based tools. There is  Zotero, and RefWorks. There is also Citavi which is also popular. ReadCube is quite used by many people I know.

I mean you can really choose anything as long as it makes your life easier not harder. Choose what suits you! These tools are created to automate and facilitate the referencing process. Invest learning your software of choice before starting your PhD. The majority of these tools facilitate the insertion of citations in your text. They take care of everything and automate the whole process for you. They allow you to organise your bibliography and to annotate and catalogue the academic papers you have read or the ones you end up including in your reports or thesis. In addition, you can with few clicks  change your bibliography style.

All these tools integrate very well with the majority of word processors via plugins. You need per example to install the Mendeley plugin in MS Word.

Please do not forget to consult at least a referencing guide book even when using these automated tools since a lot of them produce in many instances incomplete or incorrect citations. You can also seek the help of websites such as Cite This for Me and citefast. They would help you to manually fill out the details of a citation and then to export that citation to a format suitable to your bibliography management software such as Endnote or Mendeley.

If you are doing a PhD in the sciences, there is a big probability that you will be obliged to learn LaTeX & BibTeX/BibLaTeX especially here in  the UK because there is a big probability that your supervisor and the academics in your research group will force you to use LaTeX for writing academic papers, reports, presentations, posters, thesis vel cetera. Please ask your prospective supervisor and other PhD candidates in your research group whether knowledge of LaTeX is essential.

LaTeX has a bit of a learning curve so it is strongly advisable to learn it before stepping into a PhD.  In addition, all journals and conferences proceedings require LaTeX and in few instances I saw, some journals give authors only a LaTeX template to work with. Usually, you would have two conference templates one in Microsoft Word and the other is in LaTeX.

E – Master LaTeX, BibTeX and BibLaTeX

NB: You can skip this section, if you do not plan on using LaTeX/BibTeX or BibLaTeX for your PhD. Usually this is the case of a PhD in the humanities and a number of disciplines pertaining to the social sciences. Few instances of PhDs in natural sciences might not need the knowledge of LaTeX. It depends! In addition, If you used heavily LaTeX in the past, you probably know much of the material presented in this section.

Technical note 1: LaTeX pronounced «Lah-tech» or «Lay-tech», is a typesetting system and a typesetting language. According to the LaTeX project website, LaTeX is the “de facto standard for the communication and publication of scientific documents”.

Technical note 2: BibTeX and BibLaTeX are two very famous references management software and languages.  BibLaTeX is more advanced, more fancier than the good old BibTeX. Actually, BibLaTeX is considered the successor of BibTeX. Both in a technical sense are TeX packages that come by default these days with all the major distributions of LaTeX. BibTeX has an executable or a compiler that is run behind the scenes when you compile your LaTeX document. This compiler is used to parse your BibTeX bibliography notations which represent your sources (papers, books, online material etc…) i.e. entries in the .bib file. Same for BibLaTeX but the executable or the processing program is called Biber. You do not have to worry about any of this as both of them are available by default in major TeX distributions. BibTeX is very old but still used. BibTeX is great for numerical citation styles such as IEEE, ACM etc… but can be extremely annoying when it comes to using an (author, year) citation style  such as (Bakri,2016) [Harvard, APA etc…] or a label-based citation style (a.k.a alphanumeric) such as [ABC95] (ex: alpha) or superscript-based citation style such as the one used by the nature journal. When using only BibTeX, you end up being forced to use another LaTeX package such as natbib in tandem with BibTeX. Nowadays, students learn LaTeX then BibTeX  and then move on to learn and adopt BibLaTeX. I believe this is what you should do. BibTeX is still commonly used by the majority of journals & conference proceedings, this is why it is still alive. Last update of BibTeX was done 9 years ago. So in my opinion you have to learn it first (don’t be afraid there is not much to learn) even if you find yourself obliged to switch later to BibLaTeX which probably will occur at a certain time especially if you will not use simple silly bracketed numeric citations styles and where you need advanced customisations of your citations.

LaTeX allows you to create very  beautiful conference and journal articles, technical reports, books, theses, dissertations, posters, resumés, leaflets, flyers, business cards, slides presentations (through the Beamer package per example) and the list goes on and on. If you have tried to submit before to any academic workshop, conference or journal, you will realise that they usually provide you with a LaTeX template for the style and layout of the papers in their proceedings. LaTeX is distributed by the servers of CTAN (Comprehensive TeX Archive Network). In there you can actually download many individual packages (little specialised LaTeX programs that do a particular task) and their documentation.

Benefits of LaTeX (why using it – Isn’t Microsoft Word just fine?)

LaTeX is an excellent tool for producing very good professional looking documents. It a language with some learning curve. The question that you might be asking is: What is wrong with Microsoft Word or other Word processors? My answer is nothing. Microsoft Word is an amazing powerful product. You can write a PhD thesis in it and still get a PhD. If you are doing a PhD in Divinity or Literature or something like that, I believe it is not worth spending any time learning LaTeX so that the document look nicer. MS Word in such cases is just fine in my opinion.

That has been said, LaTeX has a lot of benefits and powers over Microsoft Word or any other word processor. The following list is a small taster of the benefits that make LaTeX very attractive in academia  especially in the scientific fields:

  1. The look and feel of the documents produced by LaTeX is superior to the look and feel that a Word Processor can produce. You might wander why? Well, because LaTeX is like an aristocratic lady. She knows all the etiquette concerning the right amount of spacing, the right and advisable typographical rules used in the industry of publishing, the captions vel cetera; in addition to knowing per example where to place tables or figures (normally LaTeX aims for the top of the page) among many many many formatting and style etiquette. There are lot of rules that LaTeX take control of applying them to your document on your behalf in order to make the document look so beautiful and so harmonic. Of course you can overwrite that behaviour but if you leave LaTeX do its thing, in the majority of cases,  you are guaranteed to get a beautiful professional looking article, poster, presentation, academic paper, a book or a PhD thesis.
  2. The biggest of all advantages is the separation of style of the document from the content of the document. That is huge advantage!! LaTeX ensure consistent formatting  and that the style of a document can be changed by simply changing a class file or loading some packages all with few lines. Imagine doing that with Microsoft Word and imagine you have document of 200 or 300 pages. It will impossible. So you would go pass by every page and change the styles of headings or whatever. Let me give this concrete example: You have a thesis of 300 pages all your figures captions are labelled as ‘Figure’. You were asked to change captions them to ‘Fig’ but the cross references inside the text should be “figure”. What will you do? You can do a search and replace but that will have unexpected and unwanted changes. This effect can be achieved in LaTeX in a single line. That’s it.
  3. LaTeX has advanced typesetting and apparent advantage in typesetting complex mathematical  equations and scientific notations than any word processor.  Imagine you are writing a thesis that require so much Math and symbols. You will find LateX is a lot easier for you than composing complicated equations with the equations designers  you find normally in word processors. LaTeX contains even symbols not present and could not be found in any word processors.
  4. LaTeX is the king of cross-referencing chapters, sections, subsections, paragraphs, equations, figures, tables etc… It makes it easy to generate a list of tables, a list of figures, a list of algorithms or listings or list of “whatever you want really”, and update them and customise them in all possible way. It is very easy to create an index, a glossary, a bibliography. Per example, I will speak later in the article about back-referencing a process that can never be made in MS Word in a million year. Except if they have created a plugin that I do not know about. The point I am driving home here is that LaTeX contains extremely advanced features that fills out books. In addition, if you use with LaTeX another program which also compile a sort of language called BibTeX and you say you have 400 citations (it does not matter how many references you have) and say you were asked to change the style of these citations. No problem one line. or asked to reorder them alphabetically: also no problem one line.
  5. LaTeX frankly allows you NOT to piss off reviewers of papers or examiners of your thesis by presenting to them a badly formatted document. Impressions are very important, my friend! It is human nature, you can not do anything about it. LaTeX does not even allow you to have a badly formatted document due to its nature (unless if you mess with it and overwrite things you are not suppose to do).]
  6. LaTeX is the aristocratic lady that knows how to present well what is known as f-ligatures. Ligatures always look beautiful in LaTeX.
  7. LaTeX is not bogged down (performance wise) by the amount of material in your document. Have you had the experience of your MS Word document of like 300 pages becomes extremely slow especially when you have a lot of images, tables  and cross references and  to make things even worse a Mendeley plugin keeping an eye on any changes in the locations of references? That document is bound to crash or  at least you have to wait some time until you see changes when you write. In LaTeX you do not have such problem. Actually you can write LaTeX code with the most primitive text editor, you have. All the formatting happens in the output after running the building process of LaTeX.
  8.  A lot of supervisors/advisers (98% actually in the scientific fields) demand explicitly from you to use LaTeX. They do not accept anything else. If you are in doubt, ask your prospective supervisor/advisor whether he wants you to use LaTeX, some supervisors put that explicitly on their profile pages so that prospective PhD students are aware off. If that is the case, do not step into the country without Mastering LaTeX. PhDs are very demanding and fast-paced especially in the sciences. You will find yourself writing to your supervisor/adviser every week or two a report of some sort.
  9. Certain features necessary for writing PhD Theses or Masters  Dissertations are simply not present in MS Word.  Numero Uno) Do we have short captions and long captions in MS Word? of course not.  I always look at those ugly MS Word theses of few people who decided to be stubborn, where the list of Figures or List of Tables have these gigantic captions that spans lines and lines screaming out. First, if your caption is that long it means something wrong. Either you need to divide your tables or figures into subcomponents or you are putting some sort of legend that will appear in list of figures or list of tables at the beginning of the thesis. In LaTeX, you can have for each part, chapter, section, subsection, subsubsection, paragraph, subparagraph, figure, table, equation, theorem vel cetera, a short caption and a long caption ( using the LaTeX code: \caption[Short caption goes here….]{Long caption goes here….} ). That means you can give a short caption that appears only in the List of Figures or the List of tables or the List of theorems, so forth, and a long caption that appears under the figure or above the table. Textual Legends under tables or above figures are very easy to do in LaTeX, you just provide another starred caption at the end of your code (before closing the environment): \caption*{Legend can be as huge as you want and guaranteed of not appearing in list of figures or tables}. The star tells LaTeX not to put the caption in the List of Figures or List of Tables. Numero Dos) Can you do back references in MS Word? of course not. Numero Tres) The power of glossaries commands in LaTeX like \gls{..}, \acrshort{…}, \acrshortpl{….}, \acrlong{…}, \acrlongpl{….},  \acrfull{…}, \acrfullpl{….}, is not present and will never be present in MS Word. LaTeX is amazing! imagine you can define glossaries and acronyms with plural and singular forms, with short, long and full forms and with a sorting order. Then when inside your text, you do not have to remember whether the acronym that is used for the first time is expanded or not; you just write the \gls{…} command and LaTeX makes sure that the first time an acronym is met, it will be exanded. I mean if this is not cool, what is cool? I can keep numbering in Spanish untill I reach thousand…
  10. The prenote and postnote of citations: things like see p.3 or pp. 23-25, cf. (Behr et al., 2001) … it is quite easy to add them and LaTeX knows whether to put a p or pp without you typing it explicitly.
  11. Multiple bibliographies in the same document is extremely easy to do in either BibTeX (the old man) or BibLaTeX (the younger man). This is needed for instances such as creating a bibliography or references list for each chapter in a book, or for each article or poem in an anthology etc… or when we want to classify or divide references based on the type of the source such as Books, articles, webography.., or based on a topic or on specific keywords or for specific authors or based on primary vs secondary sources etc… I don’t think that this is easy to do in MS word and Mendeley or Endnote per example as far as I know. PS: for BibTeX on its own you cannot do multiple bibliographies, you need additional packages such as multibib, chapterbib or splitbib. For BibLaTeX, you do not need any additional packages.
  12. Cleveref ( or clever referencing): makes cross-referencing very easy. It figures out automatically whether you are cross-referencing a figure, an equation, a table, a chapter, an appendix etc. Not to mention, you can change the style, with few lines of LaTeX code so all your “Figure” becomes “Fig.” etc.
  13. … I am sure there are many more…

Hopefully, I have convinced you why you should learn LaTeX and BibTeX/BibLaTeX.

There are disadvantages to be frank with you for using LaTeX/BibTeX/BibLaTeX. If you are using a desktop solution such as installing a certain TeX distribution and an LaTeX editor on your computer, you will not be able to track changes in your LaTeX manuscripts with your supervisors or collaborators. This problem is solved by using an online web-based LaTeX Editors/Distributions such as Overleaf  or ShareLaTeX. These web-based services are very famous these days. Creating tables especially complicated ones is, in many cases, a hindrance and concern for many writers despite the existence of many websites, tools, LaTeX packages and GUI features inside many famous LaTeX editors that facilitate that.

Major LaTeX Systems/Distributions

You need to install one of the following LaTeX Distributions or  Systems as a first step. Bear in mind some of the distributions takes a very long time to download and install due to the sheer amount of LaTeX packages ( thousands of packages). A lot of LaTeX distributions of systems allow you to install a minimum version which I totally recommend. Whenever you need a certain package you can always download and install it either separately or through built-in GUI packages manager. This makes the installation faster and takes less space on your computer.

Tex Live

Tex Live is available for Linux distributions and Microsoft Windows. It is a famous Tex distribution that is quite rich.

MacTeX

As you guessed, MacTeX is the TeX Distribution of Choice for the Mac OS folks.

MiKTeX

MiKTeX is a distribution available for Mac OS, Linux OSs and Microsoft Windows.

proTeXt

proTeXt is a distribution dedicated only for MS Windows folks. I do not like it so much but it is here just for completion sake.

TeX Editors

There are many LaTeX editors out there but the most famous LaTeX editors are  TexStudio, Texmaker or TeXWorks.  The best in my opinion and the most intelligent is TexStudio, and this is for different reasons. You can insert many snippets of LaTeX very quickly, you can have wizards to create tables (which is an annoying thing in LaTeX), You can place figures through GUI , you can create a Beamer Presentation very quickly etc…. Now one will argue that this is available also in other LaTeX Editors, this is true but what is not available in many editors is a technology called Intelligent Code Completion and suggestion. Meaning the Editor continues for you your typed commands, scans the file system for images among many other cool features.

LaTex Editors are not LaTeX. They are just dummy editors on top of a LaTeX distribution. Newbies confuse that. Actually you can use any simple text editor (Notepad, vim…) for writing LaTeX documents and then you can compile and build your thesis from the command line or Terminal. You wont be able to build your documents, If you only install a LaTeX Editor without installing an actual LaTeX Distribution.

Online LaTeX Editors

Do not rely on them for no reason other than they make you lazy. That means they are pretty cool! The greatest of all benefits of these types of editors is the fact that THEY ARE ON THE WEB,  meaning they are accessible from everywhere on the planet and no need to deal with installing a LaTeX Distribution and then installing a LaTeX Editor. Another benefit is that they all have cloud storage, similar to the one provided by Dropbox, Google Drive or Microsoft OneDrive. That means your paper, your dissertation or your thesis is stored on their system, backed up regularly (since the majority of these services have some sort of version/revision control system) and which you and your collaborators can edit, compile and comment on; all of this while remaining online. Pretty Cool! The following are the most used web editors:

Overleaf Online Editor

Overleaf is very famous among PhD and Masters students. You can upload your already written thesis in LaTeX or create a totally new one from tons of templates. The beauty of this service is that your report or thesis is totally stored on the cloud and saved every few seconds automatically. You can revert back to a previous versions if needed, since the history of changes is also stored. So you do not need to back up on a continuous basis. You can share the LaTeX thesis, academic paper or  report with your supervisor/advisor or with any of your coauthors. They can comment on different parts of the document. They can even edit if you allow them at the same time so everyone has the latest version. So there are many collaboration features in this service. You can integrate the tool with Dropbox, Google Drive and GitHub among others. There is a cool feature in Overleaf where you write your LaTeX code, the moment you finish you can see how it looks like. In addition Overleaf has features like spell checker, syntax highlighting, text folding, built-in PDF viewer… Pretty cool features! don’t you think? I should mention that the free version, give you first a 100 MB  of cloud storage space. You get additional 100 MB when you complete you profile, when you send invitation  to the application to friends (on Facebook, Twitter LinkedIn and Google Plus) and when you link your Overleaf profile to an OCRID.

Papeeria Online LaTeX Editor

Papeeria online service is massive. They have a complete TeX Live distribution behind the scene. Almost same features as Overleaf (real-time collaboration, syntax highlighting, text folding, auto-completion and spell checker, in build PDF viewer) but additionally you can write also Markdown.  They have many templates to choose from for Theses, IEEE papers, reports, presentation slides….

ShareLaTeX Online LaTeX Editor

Almost similar service is ShareLaTeX same features as previous online tools. I only like their documentation to be frank with you 🙂

Front-ends to LaTeX

Lyx – The Document Processor

Lyx is a document processor that uses the WYSIWYM paradigm (an acronym for “what you see is what you mean”) . Lyx in lay terms, is a front-end to LaTeX  (do not confuse Lyx with a LaTeX Editor like  TexStudio, Texmaker or TeXWorks… Lyx allows you to write a document the way you write an MS Word document (well to be fair almost the way you write a  MS Word document). You still have to use LaTeX terminology and LaTeX concepts. Lyx is a Kind of a GUI layer on top the LaTeX ecosystem. The cool thing is not that, the cool thing is that it spits out (Export) many formats needed (PDF, LaTeX, MS Word, MS Excel, HTML, RTF vel cetera) and takes in (Import) many format like MS Word, MS Excel, LaTeX, plain text vel cetra.

I use Lyx as an intermediary tool to convert an MS Word document to LaTeX, although not the best of conversion  tool since you find many things you need to tweak afterwards. I will give you later other better tools that do conversions between MS Word/MS Excel to LaTeX and Vice versa.

There is also BaKoMa TeX Word which is this time a WYSIWYG (What You See is What You Get) but cost money I beleive.

Other useful Tools

Detexify LaTeX handwritten symbol recognition: is a tool that helps you figure out unique characters and symbols codes through handwriting recognition (PS: you do not need a digital pen – writing with your mouse would suffice).

BibTeX & BibTeX/BibLaTeX Bibliography Management software

Both BibTeX & BibLaTeX are bibliography systems. They aim mainly to format and manage the references. They are used in tandem with the LaTeX typesetting system. The extension of a BibTeX or BibLaTeX file is .bib

Manual Construction of the .bib (BibTeX) file

What the majority of people  end up doing when using this method, is they create a file with .bib extension in a normal text editor or in one of the  famous TeX Editors (like TexStudio, Texmaker or Texworks…). For each reference;  a lot of folks write it down manually  in the bib file which is a  primitive method but  produces nevertheless a better and a more complete bibliographical entry!

The following is an example of a BibTeX entry:

@inproceedings{bakri2016virtual,
  title={Virtual Worlds and the 3D Web--time for convergence?},
  author={Bakri, Hussein and Allison, Colin and Miller, Alan and Oliver, Iain},
  booktitle={International Conference on Immersive Learning},
  pages={29--42},
  year={2016},
  organization={Springer}
}

There is a faster way: Many of us go to Google Scholar, search for the paper/book in question, choose the Cite Icon (the two quotes icon), then choose BibTeX, which leads to a BibTeX code  similar to the code above. The code would be copied into the BibTex file (.bib). This is done for every reference needed. You can imagine the size and cheer amount of references at the end of a PhD thesis or a Masters dissertation per example. You have the possibility of having many .bib files each pertaining to a topic covered by your thesis/dissertation. I like to divide my bibliography into many .bib files and  to use a lot of comments in the bib files because it keeps the references more organised under specific themes.

To cite any reference from the .bib file in the actual thesis LaTeX document, you would use the LaTeX commands \cite{BibTeX_Key}, or \citeauthor{BibTeX_Key} for citing only the authors or \citeyear{BibTeX_Key} for citing only the year, or \citeN{BibTeX_Key} for noun citations and many more…depending on your needs.

There are websites that help you cite well according to a specific referencing style and allow you then to export the citations to either EndNote or a BibTeX or to any other bibliographical format. Few examples: Cite This for Me and citefast. The problem with these websites is that the method that is used is too primitive & too manual since you have to fill manually a lot of necessary fields for citing a resource compared to using a referencing software such as Endnote or using BibTeX/BibLaTeX export features in scholarly search engines and from publishers websites but the advantage is that you know that you are following exactly the citation style you need and that your citations are both correct and complete.

You have the possibility of course of having many .bib files each for a topic per example covered by your thesis. I tend to like this method of having many .bib files because it keeps your references more organised under specific themes.

One of the major problems, with the manual approach to add citations to the .bib file is that you might add accidentally the same reference many times so there would be many redundancies. Of course you can detect them and remove them and there are also a lot of tools (ex JabRef) and Python/Perl scripts that clean up the .bib file(s) for you. Some TeX editors have features that do that also.

Be Very Cautious: Google Scholar does not always give you a complete and correct BibTeX entry code. As I said, your citation should contain if it is possible  ALL the required BibTeX/BibLaTeX fields depending on the type of the citation (book, article, booklet, inproceeding…) + as much as possible of the optional fields (the more the better). Please consult a citation guide book for how to appropriately cite a specific source if these automatic tools give you something weird. I had many publishers contact me about missing fields in references. References which I have copied the BibTeX code verbatim from Google Scholar engine.

The right way to obtain a bib code: Either go to the publisher site or use DOIs. You probably have some familiarity with DOIs (Digital Object Identifiers). Every published paper/book… has a DOI number (aimed to identify digital online resources). A DOI looks something like this: 10.1145/1595496.1562908. You need to know what is the DOI of the paper in question and then go to  http://dx.doi.org/[PUT HERE THE DOI]. This will redirect you normally  to the publisher related web page of the paper where on it you will be told normally how to cite the paper properly (there is always an export format feature for BibTeX somewhere on the page). The difference here is you are following what the publisher wants you to do when citing a paper and not what Google Scholar gives you.

To illustrate this. The following is the Google Scholar version of the generated BibTeX Code of the survey paper titled (“A survey on service quality description”):

@article{kritikos2013survey,
title={A survey on service quality description},
author={Kritikos, Kyriakos and Pernici, Barbara and Plebani, Pierluigi and Cappiello, Cinzia and Comuzzi, Marco and Benrernou, Salima and Brandic, Ivona and Kert{\'e}sz, Attila and Parkin, Michael and Carro, Manuel},
journal={ACM Computing Surveys (CSUR)},
volume={46},
number={1},
pages={1},
year={2013},
publisher={ACM}
}

The publisher BibTeX code of the same survey paper (from ACM Digital Library):

article{Kritikos:2013:SSQ:2522968.2522969,
author = {Kritikos, Kyriakos and Pernici, Barbara and Plebani, Pierluigi and Cappiello, Cinzia and Comuzzi, Marco and Benrernou, Salima and Brandic, Ivona and Kert{\'e}sz, Attila and Parkin, Michael and Carro, Manuel},
title = {A Survey on Service Quality Description},
journal = {ACM Comput. Surv.},
issue_date = {October 2013},
volume = {46},
number = {1},
month = jul,
year = {2013},
issn = {0360-0300},
pages = {1:1--1:58},
articleno = {1},
numpages = {58},
url = {http://doi.acm.org/10.1145/2522968.2522969},
doi = {10.1145/2522968.2522969},
acmid = {2522969},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {QoS, SLA, Service, description, life-cycle, metamodel, model, provisioning, quality, service-level agreement},
}

You can see clearly that the code given by the publisher is a lot more detailed and precise than that of Google Scholar. A lot of publishers allow you to export the citation in different formats with or without the abstract (Yes!, you can include all the abstract text inside your BibTeX code (abstract={…}).

The smarter Way: JabRef

JabRef is one of the tools that you say to yourself when seeing it for the first time: Why the Hell I don’t know about this??

JabRef is an amazing open source Java Graphical User Interface (GUI)  bibliography reference management system similar to Mendely, Zotero etc.. but dedicated for the LaTeX/BibTeX/BibLaTeX folks. The support between BibTeX and BibLaTeX can be toggled (File then choose ‘switch to Biblatex mode’).

If you load your existing (.bib) file(s) in JabRef (by using File/Open Library). It will detect all BibTeX redundancies and mistakes in the bib file (Amazing! right!).

JabRef version: 4.3.1 - BibTeX entries Redundancies checker
JabRef version: 4.3.1 – BibTeX entries Redundancies checker
JabRef 4.3.1 - Main Window
JabRef version 4.3.1 – Main Window

You can add manually BibTeX entries depending on different types, or copy BibTeX raw text or BibTeX Code from other places. Export references to RDF, HTML, Endnote, MS Office, OpenOffice, LibreOffice….among others file formats. You can generate a BibTeX key or use the ones provided by publishers/Google Scholar.

You can open the DOI link of any paper/book which has it in your database. You can rank your references, mark them and give them certain colours to differentiate them. It allows you to edit/create/delete Essential, Optional and Deprecated fields. You can add an abstract for a paper and you can write comments. There is an amazing feature called ‘Related Articles’ which fetch you all related articles/papers you might need to consider.

The tool has good integration with Open source office suites like OpenOffice/LibreOffice and can link to your favourite text editor or LaTeX Editor (mainly TexStudio and Texmaker). In addition, you can search and fetch from the tool itself papers/books (like searching ACM Digital Library and the like…)

BibDesk

BibDesk  is a graphical bibliography manager for BibTeX/BibLaTeX file management. Unfortunately, BibDesk is only for Mac OS Users. It is not as powerful as JabRef.

BibDesk MacOS image
BibDesk on my Mac OS

BibTeX loves lowercase! How to solve this?

Have you seen ugly references that looks like this (title of the paper is all lower case):

Ugly reference: Title of the paper is all lower case
Ugly reference: Title of the paper is all lower case

The BibTeX code of the above reference is the following:

@inproceedings{kostoska2015virtual,
title={Virtual, Remote Participation in Museum Visits by Older Adults: A Feasibility Study},
author={Kostoska, Galena and Baez, Marcos and Daniel, Florian and Casati, Fabio},
booktitle={8th International Workshop on Personalized Access to Cultural Heritage (PATCH 2015), ACM IUI 2015},
pages={1–4},
year={2015}
}

Do you know why? People copy normally the BibTeX reference code from either Google Scholar or export it from the publisher website as it is, titles of papers will normally have only one set of { } and this will show all letters of the title in lowercase. This is due to  the “brace depth” problem.
To make the title NOT appear in lower case  or make it look like what you normally find in the title of academic papers i.e titles using the ‘Smart Case’ (every word is capitalised expect conjunctions & articles), you need to tell BibTeX please can you not mess with case of the title, I want the case of the title as it is. To do that you need to use extra curly braces {… } around your title. Usually the titles of academic papers as I said are in ‘Smart Case’ format meaning the first letter of every word is capitalised except the connectors. Majority of advanced LaTeX editors have a feature to transform any phrase to ‘Title Smart Case’.

The first reference would be solved by including another set of { } around the title:

@inproceedings{kostoska2015virtual,
title={{Virtual, Remote Participation in Museum Visits by Older Adults: A Feasibility Study}},
author={Kostoska, Galena and Baez, Marcos and Daniel, Florian and Casati, Fabio},
booktitle={8th International Workshop on Personalized Access to Cultural Heritage (PATCH 2015), ACM IUI 2015},
pages={1–4},
year={2015}
}

Result:

Smart Capitalization is preserved as should be.
‘Title Smart Case’ is preserved as it should be.

LaTeX important conversions tools

…. to be expanded… to include later examples of tools to convert LaTeX to MS Word/ MS Excel and vice versa

LaTeX Tables Online Creator tools

LaTeX is very powerful this is why it is used. People do not use LaTeX because they want to be fancy, they use it because  it is extremely powerful. Unfortunately, you will discover that creating a table in LaTeX is not as easy as doing it in Microsoft Word. This is maybe the only feature in LaTeX that I feel is not efficient. Nevertheless, there are many solutions better than contructing manually a LaTeX table.

LaTeX Tables Builders inside LaTeX Editors

Inside LaTeX Editors like TexStudio there are wizards that help you create a LaTeX table (a Graphical User Interface if you want). The following image shows Quick Tabular Wizard in TeXStudio LaTeX Editor.

Quick Tabular Wizard in TexStudio (on Mac OS)
Quick Tabular Wizard in TexStudio (on Mac OS)

LaTeX Online Tables Generator

LaTeX Tables Generator is the most famous online tool used to create tables. I use this tool quite often. You can construct your table directly in the tool or import data from a CSV file ( you can save an Excel File to CSV), From LaTeX code or past your data. You can generate two styles the default table style or the booktabs table styles. I prefer the Booktabs table style. You need to  have the booktabs package installed and loaded in the preamble of your document. Booktabs table style is very tidy and beautiful and it is used in academic papers and PhD theses.

The cool thing is you can save your tables as a tgn files. The file format .tgn is a custom table generator file. You can load tgn files later if you want to change something. I always have a folder where I store all my tables as .tgn files. This is in case later I want to change the tables.

Squeezing Space in LaTeX Documents

Ah! this happens to everyone especially especially when writing a paper and you are trying desperately to squeeze space.  A great article I advise you to read is found here.

Important and Interesting Packages and Commands to Master in LaTeX

For Commands: there are a lot and I am not going to throw at you a list of some sort, won’t be useful to you.  If you read an introductory/intermediate LaTeX book (which you need to do in order to learn LateX), you will get that list anyway. After learning LaTeX at least reaching a good level (for PhD purposes), I advise you to master defining your own LaTeX commands and renewing the definition of LaTeX commands (\newcommand and \renewcommand). In my personal experience: these tasks are extremely handy. This is because you might use several commands on certain snippets of text, it makes sense organising and defining  your own new bespoke commands that allow you not to be repetitive.

My recommendations concerning learning the most important and cool LaTeX packages:

The packages that do miracles in cross-references and navigational links:

  1. Hperref package:  used for handling cross-references and produces live hypertext links (in other words for manipulating hypertext marks/links in LaTeX). This package allows you to have clickable citations, clickable titles in the table of content, clickable short captions in the list of figures and list of tables among many other features. It contains Backref package (which could be loaded separately):  This package/or option in hyperref package is awesome! I discovered this particular option not from a long time ago. It allows you to add in the references of your bibliography – a clickable back reference to the point or page in which each reference was used (i.e. Back referencing from the bibliography to the in-text citations). You can click on a reference and see where it is used inside the actual text. Microsoft Word and all word processors in the world need a million year to reach the shoe level of LaTeX
An Example of the usage of the backref feature
An Example of the usage of the backref feature. You can you can click on any page where a citation was used. If this is not cool, what is cool?

The packages for creating an index

These packages will be needed when writing your PhD Thesis. So learn them and stop being lazy.

  1. makeindex package
  2. xindy package

The packages for creating a glossary

These packages will be needed when writing your PhD Thesis. So learn them and stop being lazy.

  1. glossaries package

The packages that allows you to create amazing presentation slides:

  1. beamer package

The packages for mathematics and equations

  1. amsmath Package
  2. mathtools Package

The packages that embed movies, sounds and 3D models in your PDF from LaTeX

These are just examples for fun to show you the awesomeness of LaTeX. I wrote an article that teaches you how to embed into a PDF, Interactive 3D Models, videos, sound clips via LaTeX.

  1. movie15  and Media9 Multimedia inclusion packages: This technology was in LaTeX from a zillion years ago. The packages em­bed movies, sounds and 3D ob­jects into PDF doc­u­ments. Media9 can embed in­ter­ac­tive Flash (SWF) and 3D ob­jects (manly formats like Adobe U3D & PRC). Now Microsoft Word is able to include 3D models (with formats like gLTF and the like). Packages are only available in MikTeX and TeX Live.
  2. asymptote Package:
  3. pst-solides3d package:
  4. Sketch Package

Packages that draw 2D and 3D Graphics

These are normally used also for statistical plots by R or Python.

  1. TiKZ package contained in the pgf macro package: Most famous and most powerful. Please see my articles ( 1, 23 and 4). explaining how R export tikz plots which you can include quite easily in your LaTeX documents.

The packages for Languages

  1. Babel Package.

The packages for citations

  1. BibTeX
  2. BibLaTeX & Biber
  3. natbib
  4. multibib

Packages for cross-referencing

  1. cleveref

Best LaTeX Learning Resources

  1. The Wikibooks LaTeX: Best Resource! awesome articles covering a big range of LaTeX topics although some articles are still not completely written.
  2. ShareLaTeX Documentation: As I mentioned before I like their documentation on teaching LaTeX. Recall from a previous section of this article that ShareLaTeX is an online LaTeX Editor. The documentation here is not complete or exhaustive but easy to read.
  3. Dickimaw LaTeX Books and Articles: Mainly 3 Books: (1) “LaTeX for Complete Novices” , the book covers how to write a PhD thesis in LaTeX and is a marvelous book: (2) “Using LaTeX to Write a PhD Thesis” and “LaTeX for Administrative Work“. The 3 books mentioned are online, free and have PDF versions if you want to print them. They all come with many LaTeX exercises and solutions and all the source code used in the books can be downloaded. I totally recommend them, they did miracles to my understanding of LaTeX when I started learning it.
  4. The Not So Short Introduction to LATEX and the An essential guide to LaTeX usage. Both of these guides are somehow old now but nevertheless contain good material.
  5. CTAN for checking documentations of individual LaTeX packages or individual document classes since books covers only common packages. You always can use the terminal/command line “texdoc” tool to fetch the PDF guide of any package. Type per example, on a terminal/command line “texdoc memoir” to get the documentation of memoir.
  6. There is a lovely FAQ (Frequently Asked Questions) web site with tons of material on LaTeX and its related tools (BibTeX, makeindex, AMSTeX…) which can be found at texfaq.
  7. I recommend the following books if you need more a more in depth understanding of LaTeX: The Latex Companion, Guide to LaTeX (Tools and Techniques for Computer Typesetting), The LateX Web Companion (a somehow old book (1999) that contains explanations of a lot of cool stuff including how to use LaTeX for Web publishing etc… ) and The LaTeX Graphics Companion (2007).
  8. I will be publishing on Udemy a 21 hours+ introductory course on LaTeX/BibTeX. It will be my first online video course. Hopefully it would be enjoyable and useful for students. I will add the link here later whenever the course is ready and published and I will add coupon discounts here for you to enrol and get a good discount. I was thinking of making this course completely free since I will not be able to have the time to manage it or to support students in terms of answering their questions on a regular basis but then the course took considerable time and time is money. I might change my mind later who knows! I am known to be a crazy man. I have recorded almost half of the lectures now. The course will covers all the basics (Document Structure, Page Layout, text and paragraph formatting, colours, fonts, lists, languages (including How to write in Semitic languages), rotations, tables, figures, floats, captions, headers and footers, footnotes, hyperlink and URLs…), Mathematics, Theorems, notations from some scientific fields, Listings, how to create a glossary, an index, mastering BibTeX, BibLaTeX, bibliography (including hyper-referencing and back referencing), a big number of tools/editors/online editors, a complete section on Tikz, all sort of integration between R and LaTeX (tables, plots) and vice versa, All sort of integration between GNUplot and LaTeX and vice versa, all integration between the Python Matplotlib and LaTeX and vice versa, how to embed 3D models, videos and sound clips in final PDF, Transform LaTeX into HTML and Vice Versa, Transform LaTeX in rich text and MS Word Document and Vice Versa. How to load data from a CSV file or from a datatool (.dbtex) file or from SQL database and how to manipulate the data (sorting etc…). There is a section on Beamer. I will touch a little bit on Macros and how to create packages. I have a complete section on Templates (that teaches you how to create a Curriculum Vitae, an invoice, a thesis, a dissertation, a poster etc…). In addition, I have sprinkled couple of quizzes to test your knowledge and there are many exercises we do together.  Realise all this time and I did not even dare to call the course other than an introductory course.

F – Master writing good Literature Reviews

You need descent Literature Reviews for many things: for creating research proposals, for publishing academic papers, for your own research and your thesis etc…

Please refer to an article on how to write good critical literature reviews that I have wrote. This article is now a recommended reading by many supervisors & lecturers to a lot of postgraduate students in many universities which was astonishing to me and have put a lot of stress on me to tell you the truth. I was writing for myself and for few of my blog readers, now it is like I am writing for complete classes of students.

Disadvantages and sufferings of the folks outside Academia

The great disadvantage you have when being outside academia is that unfortunately you DO NOT HAVE ACCESS to  academic papers published in reputable publishers (as an example for  Computer science: you do not have access to ACM digital library or IEEE or Elsevier or ScienceDirect  or whatever other publisher). Some academics particularly some advisers are hypocrite in the sense that some of them even have the audacity to wonder why it is difficult for someone outside academia to write a literature review in a PhD research proposal? Why they are wondering? You are sitting on your computer somewhere in Africa, or Asia, you finished your undergraduate or even Masters from a long time and you suddenly need to create a PhD proposal and review a literature which you do not have access to it?

One of the most important aims of a PhD research proposal is to propose to further the understanding or fill out particular gaps in the Literature. The only tool to systematically and scientifically discoverresearch gaps” or what is known as “research void” is through a rigorous and coherent Literature Review (whether Qualitative or Quantitative)[1].

One question for any reader that has an IQ > 80. How the hell are you going to do a rigorous and coherent Literature Review for anything actually not only for PhD research proposals, if you do not have access to the damn digital libraries of your field in the first place!!!???

This applies to all disciplines. Some digital libraries in Computer Science such as the ACM Digital Library;  allow you to pay like 100 USD or something similar in a year to have  access to all their papers. Not cheap but not a big deal either. I respect them for doing this. Others, digital publishers libraries like Elsevier- Science Direct,  Springer-Verlag, Sage Publisher, Wiley Online Library,  IEEE Xplore, Routledge and many others, have subscriptions with prices per year that are ridiculously high, actually these subscriptions are only affordable by big academic and research institutions. Not all who are applying for a PhD are currently doing a degree in such institutions or are part of those institutions. The majority of universities in the 3rd world can not afford the pricey subscriptions required by many of the publishers. Not to mention a lot of the readers of this article might be in the industry and want to do a PhD.

Recently, IEEE Digital Xplore is even considering not showing the abstract of a paper if you are not signed in or have particular access (I wonder how people will know whether they should buy the damn article! if the only information they have is the title of the paper – this is outrageous!). Not to mention, due to the enormous hypocrisy found in academia, the majority of abstracts of academic papers are just pathetic sales pitch. I am  still talking about reputable journals and conferences not low quality ones. I could mention different examples of such papers but I do not want to be sued by authors. I saw a lot of hypocrisy in academia!  and this is at the top universities. Makes you wonder what is happening at the bottom of the ranking ladder.

Actually, in simple terms, can you buy over 300 papers to do a descent PhD proposal? How unfair is that?  How much hypocrisy do we have in academia? I mean this is why the Open Access movement is extremely important in academic research. I am ready and happy to pay double for conference fees, just to make my academic papers OPEN ACCESS.

This is why the best shot for a student (mainly an overseas student) is EITHER to have someone who can download a considerable set of papers that are relevant for him or her OR to have a willing prospective supervisor to help him/her in writing up the PhD Research Proposal or at least help in acquiring the necessary reading material in the research topic.

Now there are some universities that offer members of the public access to their libraries. Some offers memberships. Many of them can give you a membership card which allows you to borrow a certain number of academic books. Some university libraries can give you an computer account that allow you to access libraries computers. Now such computers are connected to the University internal network and thus would give you by consequence access to journals. Again this option is not widely available in all countries or for all the universities. Here in UK, I saw only few universities do that. I greately admire and respect these universities! In UK, you might need what is know as a National Entitlement Card to obtain a library membership in such universities.

Side Advice: If you are coming from a 3rd world country, it is better for you, to do a Master first in the University that you are targeting for a PhD. If you manage to do that, make sure you take advantage of your Masters to acquire the skills needed to undertake a PhD and make sure and that you meet and know all the staff of your school in other words do not isolate yourself. In this way you can choose with deliberate thinking, a stable and descent supervisor/adviser that you know with certitude that you can definitely do a PhD with him or her. Make sure you both get along very well (you will work with him/her at least 3 to 5 years), make sure also that he/she is not a moron, or an egomaniac and make sure they have research funds or they can help you in applying for a scholarship.

Please have a look at Skill A in this article which many consider it, as the most important among all the skills, because unfortunately in the British System, supervisors are given sometimes powers that should not be given to them. There is this stupid centrality of the system in the role of a supervisor (if things does not work out (maybe because of the supervisor behaviour or wrong doing not because of you necessarily), things might go south for you. Every place on the planet except UK and few other countries, call these academic staff  “advisers” not “supervisors” and that is done for a self-evident reason which is to emphasise what their main job or duty is. There is a big difference in semantics between being an “adviser” for someone and being a “supervisor” on someone. After all in UK PhDs , we already have reviewers who have as main duties to objectively  judge students’ performance and judge the progress of their research, in addition, in the British system, reviewers (same applies to examiners) must not be supervisors or previous supervisors of the PhD candidate and this is in the aim of achieving complete transparency and objectivity. Add on that, reviews for a PhD candidate occur every year once or twice. So in my opinion, it is a stupid and redundant role given to the wrong person who his/her job should be on your side (an advisory job), like a consultant who has a vast research experience that you do not have.

Some bad supervisors act as if the PhD candidates are their own employees and that is strongly against universities policies especially if the doctorate is not an industrial one. We should lobby so that this terminology is completely removed from the UK academic system, because it is misleading.

I am writing an article about how to protect yourself from the beginning from hypocrite academics in your PhD and I stated a lot of good advice about the type of supervisors you might encounter normally in UK. The article is quite fun and rich in advice and information. I am not the only author, other people are joining in. The article is still in protected mode until I finalise the material in it.

G – Master Research Methods and Methodologies

I strongly advise you to read from cover to cover at least one book on research philosophies, methodologies, methods and instruments in your specific field of research before you start your PhD.

You need to know how to formulate your research questions, and hypotheses? how to plan and design your research on a solid ground? What methodology and methods will you espouse and why? You need to defend the choice of your research methods and in similar vein, you need to be aware of the criticism that each method has since your PhD will be assessed on the integrity and validity of your adopted research methodology. Research methods books are very divers. They are also very essential since they explain and expose the pros and cons from the literature of each research method in your particular field of research. These books usually contain few chapters to help you write a research proposal although there are dedicated books for that particular task.

Some fields have their own research methods books. Per instance, if you will be doing a PhD in Human Computer Interaction (a sub-field of Computer Science), there is a great book titled: Research Methods in Human-Computer Interaction.  It is not a big book. I have read this book from cover to cover and I found it well written and very easy to understand.

There are many research methods books for fields of psychology, psychiatry, economics, management, finance, business, medicine, architecture etc.., you name it, even many on sub-fields of disciplines. Some books are specialised in only one particular research method such as Grounded Theory or ethnography etc.. across many disciplines. That has been said you will also find many general high-level books on the topic of research methods/methodology that are not specific to any field or discipline. I usually find them too vague, boring and contain a lot of fluff so I strongly advice you to stick with the ones pertaining to your particular research topic/field.

Before you start doing any research, I strongly advice you to read at least one research methods book as I mentioned before. The first thing you should do to tell you the truth! You should know the difference between  “research methodology“, “research method“, “research paradigm” and “research instrument/tool“. You should also know very well the different research methods in your field of research. Actually the validity of anything you find, is based on the soundness of the methodology, the methods and the instruments you employed to come up with your results and Ph.D examiners love spending a lot of time hammering you with methodology/methods questions!!!! My supervisor was examining a PhD thesis in a Viva Voce and he told me he spent 45 minutes just asking the candidate questions about the methodology chapter, so be aware!!!

Few examples of research philosophies: The positivist philosophy, The interpretive philosophy, the phenomenology philosophy, the participatory philosophy etc…

Few qualitative and quantitative methodologies could be: experimental/empirical, survey, developmental, action research, ethnography, critical ethnography, case studies, evaluations, historical research, policy research etc…

Many candidates do not even know what is the difference between using inductive research approach, deductive research approach or abductive research approach (Deduction/Induction/Abduction). A lot of people have never heard even of the word “abduction” or abductive reasoning/abductive research. If you do not know, do not worry, have a look at these lovely YouTube Videos (Video 1, Video 2Video 3, Video 4, Video 5) that explain the difference very well.

You will definitely be asked a lot of questions concerning methodology in your Viva Voce oral examination. Per example, suppose you have collected a lot of data, have studied the patterns and have formulated a theory that tries to explain the patterns in your data. Someone might ask you: what did you use, an inductive or an abductive approach?  what will you reply? Abduction is a form of induction actually the form that represent “Inference to the Best Explanation“. So which one did you employ? Have you thought of that?

Sampling questions tend to appear quite often in Viva Voce examinations. What type of sampling (probability/Non-probability) have you used and why? It is important to have a cogent rationale (the why?) of everything you do in your research. There are some software that allow you to calculate the minimum sample size required for different statistical analyses. Questions of validity and reliability are also quite common in Vivas (can your research be replicable?) . A research methods book in your particular field will tackle and explain all this so reading one or more books before you start your PhD is of the utmost importance.

Please refer to the thesis assessment and Viva Voce preparation article for a lot of questions that were asked in UK Viva Voce examinations and for tons of advice. The article is quite rich and I believe it even merits being read before a Ph.D so you can prepare very well and know what to expect. I assure you that you will find many of the questions asked in Vivas cover methodological research approaches.

In addition, you can enroll in courses that teach research methods in your field of research. Many schools and universities around the world offer such courses at an undergraduate degree level and at a postgraduate degree level (Masters and PhDs) so please take advantage of them. Similarly, I know also that unfortunately a lot of universities and graduate schools around of the world don’t offer them and students are not even aware of the research methods in their fields. The problem is that in the American credits system, which is used by the majority of universities around the world, a lot of people can opt out of taking in my opinion essential courses (such as a research methods course) and still graduate. Even here in the UK, for many disciplines, curricula do not cover research methods not in the sub-honours level, nor in the honours level nor even in the Master level which is weird and stupid. Imagine!

In addition, you can enroll in workshops or even online video courses covering research methods on many platforms just to mention few examples such as Coursera (please check this list) or Udemy (please check this list). Although I beleive the current available online video courses on the topic are very limited and in many cases even very primitive so reading books and published peer-reviewed papers is the only way to go for the unfortunate lay person. I have been told by few colleagues about epigeum but I am not sure how much use they can be, especially if your institution is not subscibed to their programmes.

The following sections present few suggestions of books and online resources on research methodologies and methods that are cross-disciplines and research methodologies/methods specific to certain fields:

Good Books that are cross disciplines

You can of course buy one of the following general high-level books  but I believe if you are researching a specific field, it is better for you to buy (or borrow from the library) a book that covers directly your research field instead of reading a book that is too general when it comes to covering research methods. Ask your supervisor/adviser, or other academics or PhD candidates or cognoscenti in the field to suggest a good research methodologies/methods books and academic papers to read:

  1. Research methodology: Methods and techniques by Kothari
  2. Research methodology: A step-by-step guide for beginners by Kumar [Covers health, education, psychology, social work, nursing, public health, library studies and marketing research]
  3. How to research by Loraine Blaxter
  4. Introduction to Research – Understanding and Applying multiple strategies by Elizabeth Depoy
  5. The Good Research Guide – For small-scale social research projects by Martyn Denscombe (6th Edition) – a very good book for the social sciences folks
  6. Quantitative Methods in Social Science by Stephen Gorard [cross discipline book but only for social sciences such as economics, sociology etc.]
  7. Keywords in Qualitative Methods by Michael Bloor et al.
  8. Your Research Project by Nicholas Walliman
  9. Case Study Research and Applications: Design and Methods by Robert Yin

Online Video courses/YouTube Channels

There is literally thousands of research methods books specialised in thousands of disciplines and fields of research. The following is a very small list of books pertaining to very few disciplines (Computer Science, Information Technology, Management, Finance, Accounting, Psychology/Psychiatry, Medicine/Health etc…). Please ask for suggestions of good discipline-specific research methods books to read before you start your Ph.D from previous Ph.D candidates, your supervisors/advisers and other researchers.

Computer Science Methodologies/Methods Books

There is a scarcity of books that cover research methodologies and methods in Computer Science as a whole (i.e. books that play the role of an encyclopedia of research methods or a textbook that “rule them all” if we can say that). Nevertheless there are few awesome people who wrote books on research methodologies/methods for their respective sub-fields of Computer Science. Please refer to the article I am writing about research methods in Computer Science and how to write a research methodology chapter in CS  for more details and for more resources on the topic.

Human Computer Interaction (HCI)
  1. Research Methods in Human Computer Interactions by Jonathon Lazar et al. – I read this book cover to cover and I find it quite informative! this is book is recommended by the majority of HCI supervisors. A must read if you will be doing research in HCI. It also helps a lot folks who are investigating topics such as Quality of Experience (QoE), Usability/Accessibility and other research topics which are similar in nature.
  2. Research methods for human-computer interaction by Paul Cairns and Anna L. Cox
  3. Doing Better Statistics in Human-Computer Interaction by Paul Cairns
  4. Universal methods of Design – 100 Ways to Research Complex Problems, Develop Innovative Ideas, and Design Effective Solutions by Bella Martin and Bruce Hanington – to tell you the truth this book is for both industry and academia with an emphasis more on industry. The 100 methods explained here are used in a variety of user design/user testing scenarios. I read this book cover to cover and I learned a lot. It is not really a research oriented methods book in an academic PhD kind of sense such as Lazar et al. book but despite that it will provide you with several research methods used in industry that might be applicable to your research. NB: Each method is explained very briefly.
Artificial Intelligence
  1. Empirical Methods for Artificial Intelligence by Paul Cohen
System Performance, Simulation and Quality of Service Methods

Many supervisors in UK universities recommend Raj book to their students to read. If you are doing a PhD in Computer Networks’ Quality of Service (QoS) or any system performance studies, the following list is for you:

  1. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling by Raj Jain – This book is the holy grail on the topic.
  2. Measuring Computer Performance: A Practitioner’s Guide by Lilja
  3. Systems Performance: Enterprise and the Cloud by Brendan Gregg
  4. Performance Modeling and Design of Computer Systems: Queueing Theory in Action by Mor Harchol-Balter
Some academic papers on Research Methods in Computer Science

For more academic papers, please check out the article of How to write a research methodology chapter in CS.

  • Saunders, M. N. K., and P. C. Tosey. “The layers of research design.” Rapport Winter (2013): 58-59 [This paper was suggested as a reading by my supervisor, it is not CS related but it contains useful info covering a lot of fields].
  • Demeyer, Serge. “Research methods in computer science.” ICSM. 2011.
  • Dodig-Crnkovic, Gordana. “Scientific methods in computer science.” Proceedings of the Conference for the Promotion of Research in IT at New Universities and at University Colleges in Sweden, Skövde, Suecia. 2002.
  • Perry, Dewayne E., Adam A. Porter, and Lawrence G. Votta. “Empirical studies of software engineering: a roadmap.” Proceedings of the conference on The future of Software engineering. ACM, 2000.
  • Easterbrook, Steve, et al. “Selecting empirical methods for software engineering research.” Guide to advanced empirical software engineering. Springer, London, 2008. 285-311.
  • Höfer, Andreas, and Walter F. Tichy. “Status of empirical research in software engineering.” Empirical Software Engineering Issues. Critical Assessment and Future Directions. Springer, Berlin, Heidelberg, 2007. 10-19.
  • Amaral, José Nelson. “About computing science research methodology.” (2011).
  • Holz, Hilary J., et al. “Research methods in computing: what are they, and how should we teach them?.” ACM SIGCSE Bulletin. Vol. 38. No. 4. ACM, 2006.
  • Hassani, Hossein. “Research methods in computer science: The challenges and issues.” arXiv preprint arXiv:1703.04080(2017).
  • Shaw, Mary. “What makes good research in software engineering?.” International Journal on Software Tools for Technology Transfer 4.1 (2002): 1-7.
  •  Zelkowitz, Marvin V., and Dolores R. Wallace. “Experimental models for validating technology.” Computer 31.5 (1998): 23-31.
  • Runeson, Per, and Martin Höst. “Guidelines for conducting and reporting case study research in software engineering.” Empirical software engineering 14.2 (2009): 131.

Information Technology (Management & Technology)

Three volumes that are very detailed set of academic paper from the Journal of information Technology (JIT)  – just waiting for a good Samaritan to digest, compact them and put them in a textbook for students.

  1. Enacting Research Methods in Information Systems – Volume 1  (covers Critical Research, Grounded Theory, and Historical Approaches)
  2. Enacting Research Methods in Information Systems – Volume 2  (covers Interpretive Approaches and also explores Action Research)
  3. Enacting Research Methods in Information Systems – Volume 3  (covers Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research)

Business, Management and Marketing Research Methodologies/Methods Books/online resources

The best and greatest book on the topic which is the classic of the classics, is the book of Bryan & Bell – a must read for the folks in Business, management and even economics. The following list elucidates few recommended books for Business research Methods:

  1. Business Research Methods by Emma Bell, Alan Bryman and Bill Harley – newer edition Bill Harley wrote few chapters – if you are in a hurry you can buy the old 2011 version.
  2. Research Methods for Business Students by Saunders et al. is a GREAT  book on research methods. If half of the supervisors in UK recommend Bell & Bryman book, the other half recommend this one.
  3. Mathematics for Economics and Business by Ian Jacques – Now this book, according to a friend of mine who is doing a PhD in Economics, was strongly suggested for reading by her supervisor together with Bell and Bryman book.
  4. A dictionary of business research methods by John Duignan (kindle version and paid online version)
  5. Research methods for managers by Gill and Johnson
  6. Handbook of qualitative research methods in marketing by Russell W. Belk.

Online Video courses/YouTube Channels

  • Research Methods for Business Students Udemy course – is somehow of average quality. The literature review module of the course is too primitive and wrong. The course is only a small taster video course when it comes to PhD candidates and is not authoritative in anyway as the instructor clearly states it is for a Masters and Undergraduates. I think it is also a taster for Masters. The course is based on Saunders et al. book (Research Methods for Business Students), it misses many things including research philosophical stances. You still need to read Saunders et al. book or one of the other classical textbooks I suggested previously.
  • YoutTube channel called Meanthat has a playlist on research methods for business.

Finance, Economics and Accounting

  1. Research Methods and Methodology in Finance and Accounting by Bob Ryan
  2. Research Methods in Accounting by by Malcolm Smith
  3. Handbook of Research Methods and Applications in Empirical Finance by Bell et al.
  4. Research Methodology in Applied Economics: Organizing, Planning, and Conducting Economic Research by Ethridge
  5. Mathematics for Economics and Business by Ian Jacques – Now this book, according to a friend of mine who is doing a PhD in Economics, was strongly suggested for reading by her supervisor together with Bell and Bryman book.
  6. Quantitative methods for Finance by Keith Parramore and Terry Watsham
  7. Quantitative Methods: for Business, Management and Finance by Louise Swift and Sally Piff
  8. Analysing Quantitative Data for Business and Management Students by Charles Scherbaum and Kristen Shockley

Human Resource Management

  1. Research methods in human resource management by Valerie Anderson

Psychology/Psychiatry Research Methodologies/Methods Books

The lovely psychology discipline has a plethora of books on research methodologies and research methods. Ask your supervisor  or any cognoscente in the field to suggest a book.  Some books on research methods in the field of psychology are stated below (please if you have any suggestions, write them in the comments):

  1. Research Methods and Statistics in Psychology by by Hugh Coolican – Buy the new edition (2018) – it is amazing!
  2. Research Methods and Statistics in Psychology by by Haslam and McGarty
  3. Research Methods in Psychology by David Elmes, Barry Kantowitz, Henry Roediger III
  4. Statistical Methods for Psychology by David C. Howell
  5. Statistics and Research Methods in Psychology with Excel by Verma
  6. Research Methods in Psychology: Investigating Human Behavior by Paul G. Nestor and Russell K. Schutt
  7. Research Methods in Psychology: Evaluating a World of Information by Beth Morling.
  8. Research methods and data analysis for psychology by  Stuart Wilson and Rory MacLean
  9. Discovering research methods in psychology : a student’s guide by Sanders
  10. Analysing Qualitative Data in Psychology by Evanthia Lyons & Adrian Coyle

For Psychiatry:

  1. Research Methods in Psychiatry  by Chris Freeman and Peter Tyrer

Information Studies, Knowledge/records Management and Library science

  1. The Facet LIS Textbook Collection: Research Methods in Information by Alison Pickard – this book is great. I learned a lot from it. It has very easy explanation of the research philosophies. It has a great chapter on reviewing the literature which contains good advice. This book teaches you how to choose your sample, teaches statistical tests among many other goodies.
  2. Research Methods in Library and Information Science by Connaway and Radford.
  3. Research methods in information by Alison Jane Pickard

Political Science

  1. Political Science Research Methods by by Johnson and Reynolds
  2. Political Research: Methods and Practical Skills by by Sandra Halperin and Oliver Heath
  3. Research Methods in Politics by Burnham et al.

Geography/Human Geography

  1. Research Methods Geography: A critical introduction by Gomez and Jones
  2. An Introduction to Scientific Research Methods in Geography and Environmental Studies by Daniel Montello and Paul Sutton
  3. Qualitative research methods in human geography by Iain Hay

Sociology

  1. Social Research Methods by Bryman
  2. A dictionary of social research methods by Mark Elliot et al. (kindle version and paid online version)

Anthropology

  1. Research methods in anthropology: qualitative and quantitative approaches by H. Russell Bernard

Law

  1. Research Methods in Human Rights by Bard A. Andreassen

Education/Languages

  1. Research Methods in Education by Cohen et al.
  2. Research Methods and Methodologies in Education by Coe et al.
  3. Introduction to Research Methods in Education by Punch and Oancea
  4. Research methods in language and education by Kendall A. King
  5. Visual research methods in educational research by Julianne Moss and Barbara Pini
  6. Research methods for applied language studies by Richards et al.
  7. Research methods for English studies by Gabriele Griffin

Theology and religions

  1. The Routledge handbook of research methods in the study of religion by Michael Stausberg and Steven Engler

Encompassing many social sciences fields/Too Specific

  1. Research Methods in Indigenous Contexts by Arnold Groh.
  2. Research Methods in the Study of Substance Abuse by VanGeest et al.
  3. The SAGE handbook of social media research methods by Sloan and Luke
  4. Handbook of qualitative research methods on human resource management : innovative techniques by Keith Townsend et al.
  5. Research methods for creating and curating data in the digital humanities by Matt Hayler and Gabriele Griffin
  6. Research methods for reading digital data in the digital humanities by Gabriele Griffin and Matt Hayler

Health, Medicine & Nutrition

  1. Introduction to health research methods  by Jacobsen
  2. Research methods in health: investigating health and health services by Ann Bowling
  3. Clinical research methods for surgeons by David F. Penson and John Wei
  4. Handbook of health research methods : investigation, measurement and analysis by Ann Bowling and Shah Ebrahim
  5. Nutrition Research Methodologies by Lovegrove et al.

Pharmacy

  1. Pharmacy practice research methods by Zaheer-Ud-Din Babar

Architecture

  1. Research Methods for Architecture by Raymond Lucas
  2. Architectural Research Methods by Linda N. Groat

Few examples of books for other fields

  1. Research Methods for Cognitive Neuroscience by by Aaron Newman
  2. Research Methodologies and Practical Applications of Chemistry by Pogliani et al.
  3. Research Methods and Applications in Chemical and Biological Engineering by Pourhashemi et al. – This book is not released yet and I found it in an online search.
  4. Engineering Research Methodology: A Practical Insight for Researchers by Deb et al. – This book is not released yet and I found it in an online search.

Books that cover in detail only one research method (across many disciplines)

These types of books are targeted to a specific research method across many disciplines, in other words, not really tied to a particular field.

Grounded Theory

The bible on grounded theory is that of Strauss and Corbin:

  1. Grounded Theory in Practice – an amazing book and a legendary classic suggested as a reading by many supervisors.

There are many books tailoring specific research fields. The above suggestions were just few droplets from the ocean. Most importantly from all this, is that you MUST read at least one book like these before you start your PhD. It is very crucial, not knowing your research methods in your specific field is extremely unwise.

H – Master analysing qualitative data & the usage of a Computer Assisted Qualitative Data AnalysiS (CAQDAS) Software

NB: Applies only for the folks who will be doing qualitative research or mixed methods research.

You might end up collecting and analysing qualitative data (such as interviews material, participant observation etc..). If this is the case, you need to master a CAQDAS software to help you in analysing such data.

There are of course many considerations you should be aware of depending on the qualitative analytical technique you are adopting whether it is thematic analysis or template analysis or grounded theory etc…

Many CADDAS systems also analyse mixed methods data: the category that involves both quantitative and qualitative data. An example of such CAQDAS is MAXQDA which is in my opinion the most advanced software I have seen in this category.

CAQDAS are powerful and enjoyable to use. They can import data from PDFs, word processors and textual documents of all types, Microsoft Excel, Comma Separated Values (CSV), surveys results (including from services such as SurveyMonkeyQualtrics…), from SPSS, pictures, e-mails, memos, audio recordings, videos, web articles or wikis, bibliographic metadata of citations from Mendeley, Refworks, Zotero, Citavi, Endnote, BibTeX etc.., Notes from software such as Evernote, Microsoft OneNote etc. They can scrape, import and code textual data from social media web sites such as Twitter (importing tweets, #hastags, @authors etc.), YouTube (i.e video comments & video transcripts) and Facebook. Data  imported to these tools could be interview transcripts, academic papers, survey results, audio recordings or their transcripts etc …

These tools do very powerful analysis on your qualitative data in whatever form you want it and then visualise the data as mind maps, project maps, concept maps, word clouds (a.k.a tag clouds), cluster analysis graphs among many other  graphs that aggregate/summarise qualitative data and show them in a meaningful manner.

That has been said, these software do not do that automatically like magic! It is NOT like you throw your interview textual data in a CAQDAS and it would magically tell you all the patterns that can be discovered! We did not reach that level yet! Actually if this is the case you should be concerned since you will not find a job as a researcher!

You have to code your qualitative dataCoding in qualitative research has nothing to do with programming languages.  Coding actually is literally a whole science and there are many books covering the topic. 

In the simplest terms possible, coding in qualitative research is classifying and categorising textual qualitative data by using codes in the sense of semantic labels to define pieces of text. Coding is also a term used in quantitative research and designates using a number understood by the researcher istead of a textual categorical variable (per example: Male gender = 1 and female gender =2) and this for the purpose of statistical analysis.

Do not forget when you adopt a coding framework to make sure that you can assess its transparency and whether it can be applied by others i.e. make sure to assess its inter-rater reliability. This is commonly done by measuring the Cohen’s Kappa. Have a look at these informative YouTube videos (1, 2, 3) covering Cohen’s Kappa and these videos for calculating it using Excel and using SPSS.

As a side note: if you want to measure the internal consistency or reliability of “coding” quantitative data (Example: coding in a questionnaire’s context), you need to learn how to calculate the Cronbach’s alpha coefficient. To learn how to do that in this particular context (i.e questionnaires) please see this YouTube video using Excel and  YouTube video 1 & video 2 using SPSS. It is an easy process.

Coding qualitative data is facilitated by CAQDAS systems such as NVivo, MAXQDA, ATLAS.ti, QUALRUS among others.

Per example, suppose you have an interview text imported into Nvivo, you would pass through the text and code parts of it under semantic categories. You can nest codes and have parent-child relationships between codes. You need sometimes to code every line or two of your textual data in grounded theory research per example. Now the beauty is not in this task, actually it is daunting task. The beauty is seen afterwards because CAQDAS systems can give many powerful analysis tools both visual and non visual to study patterns and interrelationships. In addition, they provide you with a complex code-based querying system.

CAQDAS tools

Some CAQDAS systems are free and others are proprietary and cost money. The following is list of the most common tools (following the suggestion of a friend  of mine doing a PhD that requires their usage). It is advisable to take the opinion of your supervisor/adviser on what software package you should be familiar with from the start of your work or even before your PhD starts and it is even advisable to learn the software first and then start to collect you qualitative data since many of these tools dictates how data should be used, stored, coded and analysed.

QSR Nvivo

NVivo is a very famous software that contains many powerful tools for the analysis of qualitative data. It is recommend by many supervisors/advisers. The cheapest student license per year cost 60 quid (so it is not cheap!). Many universities have subscriptions for this tool since it is quite powerful and famous. The following are few resources that could help get started with NVivo.

Books

Online video courses/YouTube channels

MAXQDA

MAXQDA is super super super powerful. I have used it myself and used NVivo in a course aiming to familiarise myself with the myriad CAQDAS systems out there. I can tell you right away between the two, I would go with MAXQDA. NVivo is famous and this is why a lot of folks recommend it but I found personally MAXQDA 2018 is a lot more powerful than NVivo 12. Before you decide please take the advise of your supervisor/advisor and other PhD candidates and other researchers who did similar qualitative or mixed methods research as you will be doing in your PhD. You can have a look at what the software can do for you: here. MAXQDA allows you to analyse qualitative and mixed methods data.

Online Video Course/ YouTube Channels

  1. Qualitative Data Analysis using MAXQDA Analytics: Hands On –  is a Udemy online video course that is quite good. The accent of the instructor is a litle bit hard to understand and you will feel he is wasting time in the beginning of the course explaining the features of the software but despite that the course is rich. This is an absolute beginner course in my opinion, it barely scratch the surface of what you can do in MAXQDA.
  2. MAXQDA VERBI YouTube channel contains many video guides created by the MAXQDA team that teaches you how to use MAXQDA. Please have a look, you will be amazed by what you can do with MAXQDA.

ATLAS.ti

ATALAS.ti is another famous CAQDAS that helps you analyse qualitative data. It allows you to code, and annotate your textual data and to visualise relationships between them.

Video guides

Books

There are other CAQDAS systems out there. I can give you as honourable mentions: WordStat and QUALRUS. Check them out if you are interested.

General resources for coding, Qualitative & Mixed methods research

Please leave a comment if you have any useful suggestions that are germane to this section.

Books to read

Coding

  1. The Coding Manual for Qualitative Researchers by Johnny Saldana – this book was recommended by a lot people who I have asked concerning this topic – a must read before you start coding your qualitative data.
  2. A Step-by-Step Guide to Qualitative Data Coding by Philip Adu

Analysis of qualitative data

  1. Using Software in Qualitative Research by Silver and Lewins
  2. Qualitative Data Analysis: A Methods Sourcebook by Miles et al.
  3. Qualitative Content Analysis in Practice by Margrit Schreier
  4. Handling Qualitative Data by Lyn Richards

Online Video Courses on Qualitative Research

  1. How to analyse Qualitative data  – a Udemy course created and presented by Dr Jaroslaw Kriukow who was involved in many research projects in both the University of Oxford and University of Edinburgh.
  2. Introduction to Qualitative Research Methods

I – Master writing critical academic language/critical thinking

This is the most essential skill to have for any type of PhD or for any academic degree for that matter. It is a skill that makes you a better person. It makes you a critical and rational thinker. This is not poetry here!, this skill is crucial if you want to survive in any hypocritical environment. Some academics will throw at you all sort of logical fallacies and if you are not equipped, you might be fooled quite easily.

Learn how to detect logical fallacies and how to avoid them yourself in your writing and in your day to day life. You have no idea of how much logical fallacies I detect on a daily basis (some people find that quite charming about me while others find it extremely annoying and avoid saying anything in front of me).

You need to be able to detect fallacies committed by writers in articles and books. In the scientific disciplines, there are many fallacies committed, most common are the fallacies pertaining to the statistical fields: small sample or biased sample, rushed generalisations etc.

Please learn logical fallacies it is crucial for your success as an academic, as a PhD candidate, as a writer and as a presenter. There are many cases where I discovered that my supervisor was pulling a red herring on me to avoid focusing on a specific argument since he can not win it.

You need to be able to have full proof arguments in your PhD thesis (remember a PhD is a Doctor of Philosophy in X – X being the field). You should not commit any logical fallacies yourself. You should become a human detector of logical fallacies.

Master the detection and avoidance of Logical Fallacies

This is an extremely important skill as I mentioned before. The following are resources, books and online courses that I found  myself very enjoyable to read and which taught me a lot when it comes to this topic.

Recommended Online courses on Logical Fallacies

Recommended YouTube Channels

  • Philosophy Tube –  is an amazing YouTube channel about philosophical debates. It also contains a section for discussing many logical fallacies committed in argumentation.
  • Philosophy Vibe – is a beautiful cartoonized channel about philosophy. It contains many videos on logical fallacies
  • Teach Philosophy – contains many logical and critical thinking videos.
  • Wireless Philosophy – also an amazing channel.
  • Carneades.org YouTube Channel – Ah! Waw! this channel is tremendous when it comes to squeezing you mind. You can learn also logical paradoxes.
  • Nerdwriter1 channel – is a great channel for people who want to learn effective writing particularly essay writing.

Recommended websites

Recommended books

Master Rhetorical devices

Rhetorical devices are like a knife you can use them for good or for evil 🙂 They are the techniques used in persuasion by writers, politicians, advertisement industries etc. Learn how to use them in your writing for persuading others of your arguments. Use them in moderation and do not rely on them. A clever academic will detect quickly if the intention of the usage is to mislead. Nevertheless the best pieces of written work in history are works that have used rhetorical devices. These devices aim to make your writing eloquent and enjoyable.

Recommended resources

  • The Art of Communicating Eloquently – a great Udemy course on rhetorical devices which gives you tons of examples from actual speeches, writers etc… The accent of the instructor is a little bit difficult to understand but nevertheless  it is a very good course.

You need also the skills to write well and write “academically well” if we can say that. I have written  a great article about writing skills you need for your PhD, or any academic degree. Some of the advice in the writing skills article are present here.

Learn academic writing

Probably the majority of the writing that you did in the past was not academic. Academic writing is a science, it is a skill you actually learn and you hone with time and a lot of practice. No one was born with it and it is NOT a gift. It is a learned skill. Please refer to the article that covers academic writing that I wrote for more on this. The following are some few very enjoyable books that I have read either in part or in full when I was doing my PhD and found them very informative:

  1. Academic Writing for Graduate Students: Essential Skills and Tasks by John Swales & Christine Feak
  2. Academic Writing and Publishing: A Practical Handbook by James Hartley
  3. Handbook of Academic Writing by The Rowena Murray & Sarah Moore
  4. Writing a Graduate Thesis or Dissertation by Lorrie Blair – a small and very good book.
  5. The Quick Fix Guide to Academic Writing by Phillip C. Shon
  6. Scientific Style and Format by the Council of Science Editors

  7. Success in Academic Writing by Trevor Day

Books for specific disciplines

Many books are tailored to a specific research topic or field or discipline which is extremely convenient and important for you since you learn what is expected in your particular field of research.

NB: The following list is just a taster of what this category looks like for very few fields – you will find tons of these for a lot of disciplines – please invest in one of these books for your particular field:

Computer Science

  1.  Writing for computer science by Justin Zobel – this is very essential book for the computer science folks who are writing essays, reports, dissertations or theses.

Nursing/health/Medical fields

  1. Academic Writing and Referencing for Your Nursing Degree by Jane Bottomley & Steven Pryjmachuk
  2. Critical Thinking and Writing for Nursing Students by Anne Harrington & Bob Price
  3. Writing Skills in Nursing and Healthcare: A Guide to Completing Successful Dissertations and Theses by Dena Bain Taylor

Psychology

  1. The Principles of Writing in Psychology by T. Raymond Smyth  & Thomas Smyth – Holy cow! this book is just amazing! it is an encyclopedia on the topic, it covers scientific writing, referencing, styles, academic standards, writing essays, literature reviews, reporting statistics, also academic papers writing and publishing, qualitative research, how to present figures, tables etc… among tons of goodies – The book is dedicated for both undergraduate and postgraduate. I totally recommend this not only for the Psychology folks but for all disciplines.
  2. Critical Thinking in Psychology by Robert J. Sternberg, Henry L. Roediger & Diane F. Halpern
  3. Effective Writing in Psychology: Papers, Posters, and Presentations by Agatha M. Beins & Bernard C. Beins – an amazing book for writing papers. I know a supervisor who recommended this book for her PhD candidate to read.

Law

  1. Successful Legal Writing by Edwina Higgins & Laura Tatham

Books that teaches you how to write papers and publish

  1. Writing Scientific Research Articles by Margaret Cargill & Patrick O’Connor

Citing the right way

Trust me!  a lot of folks in academia do not know how to cite well or in simplest cases do not cite well out being sloppy not for any nefarious intention. Please learn how to cite well. There are very good books that teach you how to do that. They teach you many citation styles (APA, Harvard, IEEE, MLA, Turabian, Chicago etc…). They teach you how to cite per example an online video, a website, a lecture, an academic journal paper, a book, a chapter, a DVD etc… There are zillions of things to know. In some cases, researchers in a certain field need to use a certain recommended citation style so they would consult a referencing style book covering only one particular citation style (For example: APA for Psychology).

IMPORTANT NOTE: relying solely on referencing softwares such as Mendeley or Endnote or Zotero or on Google Scholar or the myriad publishers websites to export for you a BibTeX file or an RIS format file or whatever other format for a citation yields in many instances to incomplete and wrong citations (from my experience) – This is why these books are extremely important

  1. Cite Them Right: The Essential Referencing Guide by Richard Pears & Graham Shields
  2. The complete guide to referencing and avoiding plagiarism by Colin Neville

Finally on this topic, there are some websites that help you to cite correctly according to a specific citation style such as Cite This for Me and citefast. The problem with these is that the method used is too primitive/too manual since you have to fill manually a lot of necessary fields for citing a resource compared to using a referencing software such as Endnote or using BibTeX/BibLaTeX export features in scholarly search engines and publishers websites but the advantage is that you know that you are following exactly the citation style you need and that your citations are correct and complete.

J – Master NOT being isolated and the art & science of presenting research

Waw! this is very important. You should aim to finish the PhD while keeping your sanity intact! If you ever found yourself completely isolated in a PhD (meaning you can not talk to anybody about your research). That is bad, I mean very very very bad!!! This is a situation of real concern. If you think it is not: it is either you are dumb or whoever telling you not to be concerned is dumb.  Now of course you will ask why? There come a lot of times where I wished I have talked to an academic in my department or a colleague  or attended or submitted an academic paper before the time I did since other people (experts and less experts) will definitely point out things in your research that you and your adviser did not see. Things that might be better or maybe sometimes they mention methods that are easier or direct. Remove the idea that you adviser is always right or smart because this is not always true. I used to get sometimes the reality questions from people of the form: “why did you use X not Y? Y is a lot direct and easier?”. It is not like X was wrong but it was the method that takes more time than Y per example.

Create a network of friends from PhD candidates, postdocs, staff/other supervisors and collaborators in your school and outside your university. This is something you are the only one who can do. They can be of great help to you when you have concerns and problems with you primary supervisor or in the PhD. This group of staff/candidates can comment on the content of your reports, paper drafts, chapters in your thesis etc.  They can check whether there is a logical flow of ideas, whether the overall argument is cogent, whether the structure makes sense etc. They can attend mock presentations for conferences, mock Viva Voce etc.. and will give you tons of useful and needed feedback and advice.

I would give you the following wisdom: In a PhD, outside befriending  your supervisors, always befriend a librarian (trust me!), an IT specialist, a language specialist, other supervisors/staff, PhD candidates, postdocs, a staff who deals with your university’s procedures and regulations and a lawyer or (someone who is studying law). 🙂 Of course these would also be valuable friends also outside the PhD.

Ask a lot of questions and do not let your head being eaten

You need to be very inquisitive and ask a lot of questions to a lot of people either about their research or your own research. Of course no one will do your PhD for you, you will do it. On one hand, when you ask  a lot of people about what they think of your own research and the problems and challenges you are solving, it gives you the opportunity to have a lot of advice and feedback both technical and non-technical and a more appreciation of what you are trying to do. It also makes you see the bigger picture of things (i.e. a bird’s eye view). On the other hand, when you ask people about their own research, trust me!, this is not a futile exercise, it teaches you the skills necessary to look critically and objectively at your own research and how to defend the decisions you take from seeing how others do that when asked. In addition, it makes people remember you per example especially in conferences when you ask very good questions of course. Also there is something important I did not mention, talking to people (staff and students) is essential when you have problems with your supervisor or you PhD, since people will give you useful advice of how to rectify the matter from their own experiences before the problems become acute or before it is too late to do anything about them. In contrast, isolation leads you nowhere!

Some advisers become clueless especially when you become more knowledgeable than them in your particular area especially at advanced stages of research so you need always experts or near experts to criticise your work which is very good for you. Clever PhD students normally eats your head (figuratively of course) with their bombardment of intelligent questions and criticism and this is a great and healthy skill. You need to be like that (the initiator of intelligent questions not always the recipient of them by others).

When your supervisor or adviser  ask you why not do X now? Do not follow on command like a faceless idiot (that will be costly to you not to him/her). Ask why you think I should do X now? is it really in line with my PhD “story” or “arguments” and how? Do I have the time/resources to do X? What do you think? Has it been done before in the literature? if yes, why are you suggesting it? Shouldn’t I do an extensive literature review before? Trust me! a lot of advisers sometimes try to look smart in front of you by suggesting to you fancy or irrealistic ideas that either are in the literature hidden deep somewhere already or are un-achievable with the time frame and resources you have. You the poor guy or gal think well if the supervisor/adviser is saying it so it must be true or useful to my PhD. A PhD programme is designed to make you a real academic and an academic is a person who asks always a lot of questions and is suspicious by nature.

Presentation skills

You need to master the command of giving a lot of talks for conferences, or in front of posters (a lot of universities in UK organise every year poster sessions for PhD students), or for talks in front of supervisors and students, or for you Viva presentation vel cetera….  Learn the art of preparing an “elevator pitch”. This is very essential and you need to be confident and you have presentation skills and be confident when you present. When you talk in front of any audience for that matter, you should talk with the confidence of an authority because after all the research you have done, you are an authority, all you need is a healthy dose of confidence.

Before I move on to the next advice, according to Ph.D Symposium (ICSM 2009) it is advisable that you present or “explain you PhD topic to at least one person each day”. Now of course I think this might be a bit exaggerated but you can set goals like each week you need to present you PhD to at least one person.

I believe it is silly to say master the usage of a presentation software such as Apple Keynote or Microsoft PowerPoint but there are still a lot of people who do not use these software to their fullest potential. There is nowadays a new famous kid on the block which Prezi, a presentation software that allows you to express motion, zoom and spatial relationships among your ideas in a presentation.

As a general advice, avoid flashy animations or anything that can distract your audience in your slides. Keep them as simple as possible! You should use very short text in the form of lists, a lot of images, and a lot of tables. The point I am driving home here is that the content on the PowerPoint, Keynote or Prezi presentation slides should be just a reminder for what the presenter should talk about. You do not want your audience spending time reading the slides instead of focusing on what you are saying.

There is a new trend these days in many workshops and some very few conferences which involves forcing attendees to present what is dubbed as “PechaKucha” presentations. A PechaKucha is creating 20 slides with only 20 seconds each (6 minutes and 40 seconds in total). The point here is to force the presenter to get to the point quickly and efficiently instead of rambling around on each slide.

It all about connections!

Conferences are great places for acquiring connections that could help you out in the future in different scenarios (collaboration, choosing externals vel cetera). Events organised by your school is also another venue.

One cool thing about the life in UK universities, I guess also it applies to US universities, is the availability of students’ societies. Join the societies that you are interested in. In UK, all prestigious universities have tons of societies even you can find ones about weird hobbies (joggling or whatever). Well! joggling is weird human activity for me 🙂

Of course, don’t make this in detriment to your PhD or the necessary time that you should be allocating to your research. Student societies could be a good useful entertainment while giving you the opportunity to meet a lot of students and staff members  (not only students). Students’ Societies spans from being social/hobbies oriented, to spiritual, to philosophical, to even magical, to religious, to political and finally to societies by invitation only (sort of what the paranoid masses like to call them secret societies LOL!), you will not find the Skull and Bones or Scroll and Key Yale University societies.

K – Don’t miss out opportunities to teach

Help out in teaching, demonstrating, tutoring and marking modules taught by your school. Even help out as an exam invigilator! why not? They normally need a lot of PhD candidates to help out with semesters’ modules. You might have to take a few workshops and quick courses before you are allowed to do that.

Luckily in UK, teaching is not obligatory which might be the case in other PhD programs around the world. Teaching a course obliges you to master it (from my personal experience). “The best way to learn is to teach“. In a similar vein, do not overdo it because it will definitely affect your research and your PhD which should be your main objective (always strike a balance).

You can propose a short course taken from your special area of research. A lot of universities in UK encourage such initiatives. This might give you a change to share your research with a wider and more divers audience.

In addition, teaching experience is very good for your CV for both academic and non-academic positions (even if you do not want to continue in Academia, it shows at least that you have transferable skills).

L – Master doing only Ethical Research

I did not add this section in the initial draft of this article because I thought this should be self-evident. I decided to include few tips here as a reminder.

Avoid all forms of plagiarism. Many institutions and publishers use massive software such as Turnitin or urkund to detect plagiarism. Publishers like IEEE uses also a tool called CrossRef Similarity Check. Plagiarism is a great taboo  and a serious offence in academia. A taboo with “no return back” or no “second chance”. So it is that serious!

Furthermore, avoid any type of fabrications or falsifications of research results. This is another taboo. In addition, your experimental methodologies should allow others to reproduce the experiments and get the same results. Well!  the main aim of writing methodologies is reproducibility.

Stay always up to date with the literature so you do not end up duplicating other people’s work. Literature is not just for first year (although some PhDs in humanities and social sciences might require that the LR is done in the first year and that it should be submitted). This is because the most important criterion for a PhD examiner to tick or check in the examination form is the criterion of  “originality” or “original contribution to knowledge“. You might be replicating at the start of your PhD, other people’s work to get acquainted with the topic and that is absolutely fine and even recommended. If you discover something wrong with those experiments that no one did discover before (that is considered an original contribution). Per instance, you could have discovered a faulty dataset or a faulty experiment vel cetera.

There are other stuff you might see in academia which are completely unethical but veiled under a pile of hypocrisy. On example is putting names of academics that did not contribute anything to a paper or they have contributed little. Students are forced by pathetic supervisors to put names of other academics as a form of “scratch my back so I can scratch yours”  or “quid pro quo” (I discussed this phenomenon before).

Anyone who did not significantly contributed to writing your paper should be put in the acknowledgement section (no exception). Even a small contribution to writing the paper such as creating graphs or pictures should be put in the acknowledgement. This is not me talking; this is the IEEE publisher requirements. I am sure all other publishers have similar criteria.

IMPORTANT: Do not put names of people that you did NOT collaborate with,  or people that did NOT contribute anything to your papers even if your supervisor might tell you to do that for political reasons.

A lovely Professor in my school  recounted his experience of his first time examination of a Ph.D.  This was from more than 20 years ago. He was the internal examiner (i.e the examiner assigned from the institution itself). He looked at the candidate’s thesis and found a lot of published papers for each chapter with 5, 6, and 7 authors on them including the candidate’s name. He did not know what to do since this literally means the candidate did only a small part of the work or his/her role might be limited in the published work. So he asked the candidate in the Viva Voce concerning that and the candidate said frankly that he wrote the entirety of all the papers and did all the work and he just put those names for different departmental political reasons.

The point I am driving home here is that it is more difficult for you to defend your research in your Viva Voce using published papers with too many authors on them even if your name appears as the first author.

More authors’ names on your papers literally means that you have contributed less than what you should have contributed for a PhD. Please always bear in mind that the two main requirements of a PhD are to have (1) substantial (2) contribution to the knowledge of the field (two requirements here: the scope requirement i.e substantial (lots of it) + original contribution to the literature of the field). There are other requirements of course, please refer to this article for more information.  There is nothing wrong with collaboration and this is required and very beneficial to you  but please bear in mind that having many names on your papers will eventually hinder the “defensibility” of your work at the end of the PhD.

So appeasing your supervisor would have a very detrimental effect on you later. You supervisor has no say in your final PhD examination of the thesis/oral defence whether you appease him/her or not, whether you are loyal to him/her or not, whether she/he likes you or not. I am talking about the UK system. Of course you should aim to have the best professional relationship with your supervisor based on mutual respect but regardless of that relationship, he/she is inconsequential to whether you get a PhD or not and I think I made this point very clear throughout the article.

If your research involves assuring anonymity of participants in your experiments, make sure you anonymise correctly. Use pseudonames for institutions or organisations, and use codes or numbers instead of names for participants. Do not forget to blur the faces of everyone in your photographs before you include the photos in your papers or your thesis (if you are allowed to take photos of your research setting, equipment, people etc…) unless you have received written consents.

Many types of research require ethical approval from your university or your school ethical committee and you need to be aware of the process so you can plan ahead. These things can take a lot of time. Ethical approval for research projects that involves dealing with pupils or exterior governmental services such the NHS tend to take the longest time for approval. You do not want to spend time waiting for such process so keep that in mind.

M – Master the art and science of publishing research & take any opportunity to practice professional paper reviewing

Doing a literature review is an essential skill to have. See a great article, I wrote on how to do that systematically. Everything I did in my PhD was in the form of numerous full reports in LaTeX and MS Word. These were done in order to document an experiment conducted or to document a tool that was developed or to survey a topic. Each one of these reports contained a complete structure of an academic paper (Abstract, Introduction, Literature Review, Approach/Methodology, Results, Analysis, Conclusion). Always follow that structure! This structure is called the “logico-deductive structure“. Even if you are developing a software, try to follow similar structure with the reports you will write and send to your supervisor. This will make it easy  to publish the report if you and your supervisor are happy with the quality and suitability of the report.

Always aim to publish any work you do in the Ph.D from year 1. Original contribution to knowledge is the most essential requirement for any PhD. Your Ph.D work has to be substantial meaning lots of work of importance and impact, written and explained well, of good academic quality worthy of a PhD and should be publishable (i.e. (1) lucid, (2) scholarly,  (3) substantial and (4) contributes to the knowledge in the field). Please refer to an article I  wrote on what are the criteria of a PhD, thesis assessment and Viva Voce preparations. Ideally Ph.D candidates aim to have at least one peer-reviewed publication for every chapter. Publications tell the examiners of your Ph.D that your work was peer-reviewed and was found worthy by other scholars in your field. This is why publishing is extremely essential to the success of your Ph.D. You can still get a PhD without publishing (in UK) but with publications your defence is easier in your Viva Voce. In some educational systems around the world, you must publish in order to get a PhD.

If you have a work worthy of publishing, and you do not know the right venue, a lot of publishers have recommender systems such the IEEE Publication Recommender. Probable venues for you to publish are nothing but the relevant journals and conferences you have cited in your report. Also your supervisor and other academics in your department can suggest a lot of venues for you to publish in.

Always know what are the best journals and conferences in your field of research. I mentioned this in my literature review article. Make these journals or conference proceedings your digest reading throughout all the years of your PhD even till the end. Someone should be able to ask you on the spot: what are the top 3 conferences & journals in your specific domain of research? and you should have an answer to that. The aim is that you would hopefully publish in such venues.

Your Adviser/Supervisor should help you in this regard (don’t be shy to ask simply because s/he is supposedly more experienced than you and knows which conferences/journals are worthy and which are not).

Make these journals or conferences your daily or weekly digest from the start of your PhD and continue this behaviour through all the years of your study. You should subscribe to these conference proceedings or journals, like you do with your favourite car magazine or gaming consoles magazine. Many famous journals and conferences have email alerts and social networks’ alerts that inform you when a new issues are published.

Another good advice I can give here is that you should get an ORCID account, to protect your academic papers published and future publications. An ORCID is a persistent unique identifier for researchers. This is because a lot of people have similar names. I do not want someone with the same name like me to claim my papers. All publishers now ask for an ORCID to link your submitted paper to you and only to you. Another reason is  the fact that you might be now at University X and you have a profile there but later you would move to work in University Y. Other accounts are not transcendental like ORCID, your Google Scholar profile is at the end of the day linked to a specific academic email, same for Research Gate and Academia accounts.

Concerning academic paper reviewing, Fong [4] strongly recommends postgraduate students to find opportunities to practice professional paper reviewing. Supervisors/advisors who sit usually on conference program committees can help greately with that or you might be contacted by a journal editor to review papers especially if you have published before. Please only review academic papers pertaining to your specific topic of research and review only legitimate articles in legitimate conferences/journals. A lot of academics get bombarded on a daily basis by spam reviewing emails requesting review from low quality or spurious conferences

Actually reviewing a paper is an amazing idea, it constitute a great addition on the academic CV and it helps PhD candidates to  learn how to be critical and it helps them also by consequence to address criticism in their own PhD work. In a sense, this activity strengthen the critical mind which helps the candidates to avoid pitfalls. It is also a good preparation for their Viva Voce and PhD thesis assessment. A PhD thesis assessment is similar to a peer-review process but on a much larger scale.

N – Master managing efficiently your PhD

You should manage your supervisor as I mentioned previously and take advantage of his/her valuable time and expertise not the other way around. The supervisor/adviser is just a resource for you so take full advantage of that. In addition, there many useful ideas for managing the PhD itself. The following are few ideas that help you manage you work during a PhD:

Backup your data continuously

You never imagine how many candidates I know who where too sloppy in not backing up continuously their data, reports, thesis etc… I have one  PhD candidate in the school of chemistry asking me how to retrieve desperately a report which he stored on a USB memory stick that does not work anymore (with no other backups). We tried putting it into a bag of rice and other crazy stuff (software/hardware techniques) but nothing worked. Why be like that? You have plenty of free online backup and cloud-based storage services such as Dropbox, Google Drive, Microsoft OneDrive among many others where you can store backups. Invest in buying external Hard disks, and USB memory sticks. They are very cheap these days. Continuously backup up your data in many places. There are services and software and operating systems tools that help you in doing that automatically and intelligently. Institutions normally have backup systems for staff and students.

Create and populate a research diary/notebook

Go grab a big diary/notebook or better download one of the tools online or even create a  digital document on your PC or on cloud services. This diary will play the role of storing all your research ideas, the wrong approaches, what worked and what did not and always try to write the “why” of things, write also the lessons learned.  It could contain decisions, ideas, expectations of outcomes, papers you have read, sketches of algorithms, mind maps, code versions, theorems, sources of data, experiments , sketches of proofs etc. Trust me it will help you a lot when writing your thesis later. This diary will give you a lot of material to defend your arguments, methods and findings.  You should know that you have to defend in your Viva Voce every single decision you have made (check out the article I am writing on Viva preparation and what examiners really assess in a PhD thesis). Try to always record all research activities in a chronological order as much as possible.  It is funny when you research the end and look back at the beginning and laugh!! The diary should be reflective and critical to yourself so do not flatter yourself or what you are doing. Do not worry no body will see it if you do not want.

A full-time PhD candidate is expected to work 40 hours a week, does not matter how in many PhDs, it could be Monday to Friday from 9:00 am till 5:00 pm or 10:00 am till 6:00 am. A full-time PhD is the same as a full-time job. The beauty of the PhD is that many of them are flexible when it comes to time and that is scary because you end up working too much on one day (12 or 14 hours) and too little on another (ex: 4 hours). It is good to learn how to manage your time efficiently so you can have also a social life that will keep you sane.

Whiteboarding the thesis

One beautiful soul not from the realm of mere mortals has taught me this technique and since that time I am indebted to her. She do this normally with all her students. The activity is called “Whiteboarding the PhD Thesis“. I advise you to do this technique with your supervisor either before you start officially writing you thesis or just before the submission. The idea is to use a big whiteboard and write your chapters titles in sections of the whiteboard and then follow your arguments under each chapter. The aim is to see how your argument flow during across your thesis chapters. There is an important requirement for the PhD that examiners check on their assessment forms which is the coherence of the argument.

The PhD should have a ‘coherent story’, a ‘coherent and consistent narrative’, a ‘coherent corpus’ tackling a specific topic in depth not in breadth. This is called the coherence and consistency requirement. This also applies to a PhD by portfolio (meaning submitting a set of published peer-reviewed academic papers that follow a coherent narrative which is accompanied by a written introduction and conclusion to pull everything together). Please see a good article I wrote on writing a PhD thesis the way examiners want and Viva Preparations.

Another technique that is quite useful to learn which is the backward reasoning  technique taken from Saunders et al. [3]. This technique can be used in the thesis whiteboarding session. You start by stating clearly the conclusions and contributions of your whole PhD research then from these you would say how they are based on your findings (from experiments, tools developed etc…), then you would be probed on what these findings are based upon. This will lead backward to the method(s) used (what?how? and why?) which should be based on a research strategy that leads to valid and reliable findings. The research strategy should be built upon a critical literature review and upon the research questions and objectives. As you can see in this technique you move backward from the final research outcomes of your whole PhD to the beginnings and you check if everything is cogent.

O – Learn how to draw professional diagrams and infographics

I know what you are thinking! You are probably thinking is this really a skill for a PhD? The answer is YES and YES and YES LOL. You should learn how to use drawing software such as Microsoft Visiohttps://www.draw.io, Dia Diagram Editor, Edraw Max Pro or any other tool out there. Some people use Adobe Photoshop or similar fancy image editing tools and that is fine! Others  use Microsoft Word/Microsoft PowerPoint/Google Docs drawing tools, Inkscape, GIMP or even Paint. I found from personal experience that Edraw Max Pro is an amazing paid tool that can create many types of useful diagrams. I also use https://www.draw.io which is a good free online alternative.

Whatever tool you choose to learn, make sure you are able to draw beautiful and professional publication quality diagrams. You might need to draw design diagrams of a software if you are a computer scientist, or you might want to visualise an idea. Aim to impress your examiners/reviewers with beautiful professional looking diagrams in your PhD thesis!!!  Any tool will do the job as long as you master it and know how to draw what you need. It is better to learn that before the PhD since you do not have time to spend on learning the tools during the PhD.

You need to know how to draw Gantt charts and Gantt tables because you will use them definitely in PhD progress reports that you will submit at the  beginning or at the end of each year (some universities only require review reports to be submitted once at the end of each year of your PhD). You will need Gantt charts also to communicate timings of research tasks and milestones between you and your supervisor. A famous project management software that allows you to draw Gantt charts is Microsoft Project.

In a similar vein, there are other types of diagrams that are very important and very useful in the PhD such as concept maps or mind maps and relevance trees . They help you brainstorm ideas with your supervisor even I recommend including these diagrams in official reports submitted for yearly reviews.

In a nutshell, you need to know how to draw the following diagrams (trust me YOU WILL NEED THEM at different stages of your research and writing up) – please check that you know how to draw beautiful diagrams such as: Venn Diagrams (good to see them in the Introduction and literature review chapters, they situate the precise topic you are working on among parent topics per example), Organisational charts, Gantt charts and Gantt tables (for time management), Work Breakdown Structure Diagrams, Flowcharts, Concept Maps/Mind Maps, Relevance Trees, Block diagrams, Word clouds, Network Diagrams etc. and of course if you are doing a PhD in Computer Science, you need to know UML 2.0 diagrams, to know how to draw them and what usage they serve in the design of a software. You might also need in Computer Science – software design to know how to draw wireframes.

It is extremely amazing if you know also how to draw professionally looking infographics (please no cartoonised ones). You supervisor and examiners will love you! Infographics are used a lot in textbooks and if you can show you can replicate some or create something totally new. Bob is really your uncle I guess!!!!

I like personally to use draw.io. It is free and on the web!!! It is amazing! I use it for all sorts of diagrams that I need.  All the above mentioned diagrams are available in draw.io web tool and there are many libraries for icons, symbols etc…  You will like it!

Word clouds (a.k.a tag clouds) are also used in qualitative data analysis and many CAQDAS tools can generate them for you (see the section that talks about CAQDAS software and qualitative data analysis). Word clouds or tag clouds can also be generated by a lot of tools online. Some tools can generate them in the form of faces or shapes if you want.

A Side Note – Important advice when using draw.io – You can export a PNG, JPEG, SVG, PDF etc. version of your diagram. I find from experience, that if you use the PDF version of the diagram (the one exported from draw.io), the diagram has a better resolution than the PNG or JPEG  especially when you include it into your LaTeX document (paper or thesis or even poster). When you finish drawing what you want to do is to always select everything in the diagram by pressing Ctrl + A and then go and do the export. Do not forget to select the crop feature when you do that. This will generate an exact image with the same size as the drawing not a whole white page with a small diagram in it.

General advice

In this section, I have few advice that do not fit into a skill per se but are very useful:

  • If you are in the UK, please do not forget to take advantage of the SCONUL scheme that allows you to access the majority of universities’ libraries and borrow books from them. Your Eduroam (the world wide roaming for education & research) account will work automatically and you can access all the journals the same way you do in your own university library or in your university’s internal network.  It is ideal for the folks who live in one city or town (that has a university) and study in another university further away. A lot of PhD candidates are not even aware of this. Also beleive or not in UK, there are many very good public libraries with  big books collections many of these books are academic. Check out public libraries near you and apply for a membership card that allow you to borrow books, many libraries give you the card without any headache while others, especially if you are an international PhD candidate in Scotland, require from you a card called a National Entitlement Card. Don’t worry! it is very easy to get it from your city/town council. You need a proof of residency that shows the address and an ID such as a passport and that is it! Many of these public libraries have very good quite places to study. Trust me this is needed especially when main libaries are busy especially in exam times and of course when you have an annoying talkative office mate! When I was doing my PhD, I spent a lot of times in numerous libraries across 3 universities and across two cities’ public libraries. I found that I can avoid writer’s block when I simply change the place of where I write and work.
  • Always always always run at least one pilot study or pilot test or a pilot experiment before you do the real study or experiment – It does not matter what you are doing, if you are a social science guy/gal or a natural science guy/gal, or a CS guy/gal doing user-based studies or you are measuring metrics programatically or doing an experiment in chemistry or biology, it is the same concept, do a small pilot experiment/study or a test experiment and then go through ALL phases: from the analysis (statistics…) till the reporting phase.  Take the opinions of your supervisors and other academics on the pilot study/experiment results, analysis and on the report. Afterwards, start doing the real experiment or study. You can not imagine how many PhD candidates I know who rushed to do their actual experiments after they supposedly “designed them well” and then they realised in a very sad way that the experiments contained many errors in the methodological approach, or in the sample and so on and so forth… Pilot studies/experiments can identify potential biases and methodological problems. NB: It is recommended that the participants in pilot studies/experiments should be from the target population.
  • Familiarise yourself with questionnaires’ services/software such as SurveyMonkeyQualtrics, Snap Surveys, Surveygizmo, crowdsignal and Google forms among others especially if your research involves designing questionnaires and capturing responses from them. Many of these services contain GUI components such as check boxes, sliders, text boxes, matrices etc… that allow you to design a questionnaire in an easy way. There are usually templates ready to use. These tools also code the responses for you so that you can use statistical tools to analyse your captured data. Do not forget that you need to prove the internal consistency or reliability of your questionnaire so learn how to calculate the Cronbach’s alpha coefficiant. You should understand very well what the number is really telling you and correct your questionnaire accordingly. To learn how to do that please check this YouTube video using Excel and  YouTube video 1 & video 2 using SPSS. It is an easy process. There is also another method that is more complicated and sometimes it is more preferable which is the Exploratory factor analysis.
  • Familiarize yourself with open source platforms that help researchers design experiments, collect data and analyse results. An example is the Touchstone experimental design platform.
  • For sampling (ex: surveys/questionnaires/human experiments etc…) you might need randomization. There are many digital randomization services such as randomization.com that can help you out. Statistical packages and languages such as SPSS, SAS, R and others have also many randomization functions. You can also go old school for whatever weird reason and use random digit tables but that is extremely archaic and primitive.
  • If you are doing users-based/participants-based studies, always have a good rewarding system (good incentive) so that people can spend the required time on your project without feeling they are participating perfunctorily.  The least thing you want is for people to give you fake or rushed data. Think of a good, attractive and reasonable rewarding system and do not overlook this. Chocolates and sweets are not really very attractive especially if you want a big divers sample. Friends would be happy to help but sometimes you need a bigger and more divers sample and a convenience sample is not an option. I found Amazon vouchers to be a good rewarding system, the vouchers do not have to be 50 pounds or something like that. I mean if you have generous funding for that go for it!  Five or ten pounds Amazon voucher cards might be very useful for participants as a thank you and could attract a lot more for advertised studies.  Every funded project set aside money for a rewarding system for participants so you will not buy anything from your own pockets! But some supervisors suddenly get bursts of superb intelligence and decide to buy a lot of sweets or something ridiculous or silly like that with the money! A lot of people purchase stuff from amazon so getting a voucher discount of 10 pounds obtained from participating in a study that takes 30 minutes is not a bad thing for a participant! You can buy vouchers from Tesco (the big stores) in UK or online from Amazon!
  • You might need to use a software that copy files across computers or from a client machine to a server or vice versa (FTP/SFTP). FileZilla , Cyberduck, and WinSCP might be useful tools for you to be familiar with.  There are others of course. You might need to learn to SSH to a server from a terminal. I am saying this here, since I have a friend who is doing a PhD in Physics who asked me to help him out SSH-ing to a server where he is storing his datasets. If you are on Microsoft Windows machine, you can use a popular tool called Putty. The usage of Linux and their terminals is not only for Computing Sciences folks. I know many people doing PhDs in scientific fields such as Physics, Asrtonomy and Chemistry who have many scientific applications developped only on Linux.  If you are NOT doing a Ph.D in CS but you still need heavy usage of Linux OSs for running  scientific or visualisations applications, please have a look at Skill 1 section for some useful suggestions of online resources, books and online video courses on the topic.

Questions that might be asked at one point or another in a PhD

1- Can I copy verbatim my published papers or how much verbatim I can copy from my published papers during the PhD into the thesis in the write up?

This is an extremely common question and I asked an esteemed source on copyright matters and got some good informative answers.

First thing that comes to the mind of a careful student: am I commiting self plagiarism? The answer is clearly no, since these published papers are part of the work done for your PhD degree, they contain your own ideas, experiments etc…  and the thesis is the culminating submission of your PhD degree.

But hold on, there are two issues if ignored they will get you into academic misconduct (very serious trouble!).

First, acknowledging contributions of other authors if there is any: normally aside from the supervisor unless if the supervisor actually contributed to the work on any level. Mere supervision task is acknowledged in the thesis differently. Usually other members of the reearch team might have practically contributed to the paper so you have to exclude their contributions from the thesis and specify clearly in your thesis what have you contributed to the work published and what have you not. This is very serious! if you don’t do that you are commiting plagiarism by attributing others ‘ideas and contributions as yours. I know also for a fact that in many cases, supervisors actually force students to put names of people in their department or elsewhere who have not contributed anything (an unethical behaviour that is very common), in this case no need to acknowledge anyone who did not contribute but always have concrete proof in case something comes up in the future. Have with you always the emails documenting  all previous drafts, and the absolute intial report you wrote yourself and have sent to the supervisor (s). You never know! it is a hypocritical environment!  PhD candidates have to acknowledge also at the beginning of the thesis whether they used equipment or software of other research team members and in what extent.

Second, now aknowledging any contributions is out of the way, a lot of time even you might be the only author of the paper or the paper would have only two names: you and the supervisor (with no contribution of supervisor other than the supervisory work: i.e. feedback & comments), the questions asked now are: how much can you copy verbatim or can you copy verbatim in the first place?

The problem now is a copyright problem with the publishers. I will hand the answer to very trusted source about copyright matters from a well respected Scottish university (the given answer is verbatim) –  The source wanted to stay anonymous and I  respect that but he was happy to give his consent on sharing his response. I should mention that I started the conversation with this esteemed source on copyright matters with the fact that it is advisable for the PhD candidate to review the copyright documentation he or she have signed with the publishers.

As you say, the starting point would be for the student to check their agreement with the publisher to see what they and their co-authors agreed regarding copyright and reuse. The agreement may allow them to include the published texts verbatim in the thesis without permission from the publisher or use of a copyright exception.

If however the authors assigned copyright to the publisher, and there is no clause in the agreement allowing reuse in a thesis, the student will need the publisher’s permission to include extended verbatim extracts in their thesis. I’ve included a template for a permissions letter that could be used as a basis for this. It’s our experience that this kind of permission is normally granted for the print thesis.

It’s also worth checking the publisher’s open access/self-archiving/author reuse (or whatever they call it) policy, normally found on their website, which sometimes state that authors are free to republish in their own future works, occasionally with limitations like ‘as long as it includes x% new material’ or ‘doesn’t constitute more than X% of the total new work’.

Agreed that it’s worth exploring using versions of the published articles held in open access repositories, but bear in mind that these may also have restrictions attached, so again you would have to check the publishing agreement and reuse policies before copying from these.

The student could copy shorter extracts without permission by making use of the “Illustration for instruction” exception (Copyright Designs and Patents Act, section 32, June 2014) – however this would be subject to the limits of ‘fair dealing’ which would not allow the extensive copying you suggest. There is no precise definition of what ‘fair dealing’ is, so I can’t give an exact figure on how much you could copy under this exception – roughly speaking I’d suggest that copying a paragraph might be considered ‘fair’.

Remember also that the thesis may eventually be published online in the form of an e-thesis – the “illustration for instruction” exception would not apply in this case as placing the thesis online is considered a form of publishing. Therefore explicit permission to copy from the published articles in the e-thesis would need to be sought from the publisher if the publishing agreement and their reuse policies do not allow this. It may be possible to embargo the e-thesis for a period of time, or edit it to remove the infringing sections, if permission cannot be obtained.

It might also be an idea to check with your supervisor and School or possibly the Pro Dean about attitudes to inclusion of pre-published articles in theses, and what percentage of the thesis can consist of such articles.

If the theses ends up being submitted through plagiarism software (e.g. Turnitin) it will probably be flagged if it includes substantial extracts from published articles – just something to be aware of.

Finally, re-writing the content of the published articles to include in the thesis would not be a problem from a copyright perspective, as copyright law applies only to the text and not to the ideas expressed within it.

As the esteemed source suggested: always refer back to your university policies, and ask supervsior or the Pro Dean who they will give you further advice. The safest of the safest approaches is to re-write the content of the published articles that pertain to your own contributions (in case of papers of many authors) and then include them in the chapters of your thesis as this is completely fine since you own the ideas and the work. Rewriting might be a daunting task. I know PhD candidates with 14 published papers. It makes sense  to look at what is permitted by the publishing agreement or by your school policies before commiting to rewriting a massive number of papers.

What you MUST know if you are doing a Research based Master or a PhD in Computer Science

You must know ALL the above Major Points  because these general skills are necessary for every discipline under the Sun and for every PhD under the Sun.

The following are skills specific for Computer Science. It is also pertinent here to mention that what will be discussed later are ONLY GENERAL skills for CS so you need also to learn specific skills pertaining to your PARTICULAR research topic. You might need per instance to learn Ruby and Ruby on Rails or I don’t know maybe learn .NET framework languages or Google Go or Deep/machine Learning libraries like TensorFlow, or scikit-learn. You might write scripts in tshark or Perl. You might be doing stuff in Apache Hadoop, blockchain and Ethereum or whatever is fancy these days. I mean here it is extremely crazy, stupid and unproductive to have a fixed template of CS skills/languages to throw at you. I won’t do it and you probably won’t accept such a template.

That has been said, from my experience, the following skills are too essential and too general in the same time. They work on many dimensions if I can say that. Per Example, I have put Python as a ‘ring to rule them all’ kind of language that you need to master and I believe this is becoming very true these days. Python is used to do statistics and data visualisation like in skill ‘B – Master 1 or more Statistical Packages or Statistical Languages‘ so if you are doing a PhD you definitely need to visualise your data and do statistical tests so if you decide to embark on a Python journey, you do NOT need to learn R or SPSS or Stata for your statistics (this is a great advantage!!!). Imagine you are also doing a PhD in security,  or digital forensics or a PhD that involves machine learning vel cetera, well!!! bob’s your uncle, because there is a big probability that you need also Python as a language for your specific research topic.

I am also assuming that you have an implicit skill of having a very strong foundation in some programming language. This is of course something that is recommended to have for lower academic degrees not just a PhD. Breadth of coverage is stupid when it comes to programming languages, depth is what is really needed. Except if you are an IT consultant, I do not see how knowing 30 programming languages in a shallow way will help you in a PhD.  Master one or maximum two programming languages to the core!  Usually, newbies state on their CVs that they have familiarity in many languages, while expert programmers love and stick with only very few languages and they master them and work on tons of projects using them. Programming is all about practice & exposure, the more you do it, the better you become. No amount of books and courses can replace practice. It is like going to the gym, you do not read books on the anatomy of biceps muscles and books on weight lifting  and then expect to develop some muscles. Being very good in programming comes from a very vast experience in projects, from contributing to open source applications, from internships, from working in industry for couple of years etc.  This is why I do not judge my readers, I do not know who is reading this article but I intended this article in the first place to be dedicated for the disadvantaged around the world not for the spoiled ingrateful hypocrites living near Silicon Valley or in spoiled first world countries with easy access to programming interships & to jobs in Google, Microsoft or Facebook.  While on this subject, it makes sense and it is wiser actually, if it is possible, to choose a research topic for a PhD where you can take advantage for your programming expertise in a specific language.

Skill 1 – Master Linux/Unix, Usage of Terminals  and Shell Scripting

Every reputable PhD in a reputable Computer Science School in UK, DOES require you to master  Linux/Unix and Shell Scripting ( bash, csh or tcsh, zsh, ksh, …) or generally speaking to master the usage and command of POSIX-based Operating systems a.k.a. Unix-based or Unix-like operating systems.  Actually the mastery in this skill is required in the undergraduate level in UK not only for a PhD per se.

In the UK, Linux is everywhere meaning in all undergraduate modules from the start without exceptions. This is unfortunately and sadly not true for a lot of undergraduate/master degrees around the world especially in poor 3rd world countries. In many countries, students are given a mere glimpse or taster of Linux in per example an Operating Systems course and that is it. The usage of such a OS system is only limited to such a course. Sadly nothing more than that. Unfortunately this is extremely bad for students coming from very disadvantaged countries to study in western European countries, the UK or the US.

In my school (the School of Computer Science, University of St Andrews), all our lab PCs are equiped with a dual boot between Microsoft Windows 10 and the Fedora Linux distribution.

If you have a Mac OS you probably know you can write shell scripts and even turn your BSD based Mac OS X terminal into a complete fully-fledged GNU Linux terminal through installing additional packages from packages installers like  HomeBrew, MacPort and Fink.  I use in this section the GNU Linux as a representative subcategory only.

By shell scripting, I don’t mean writing silly scripts. You should master scripting with powerful GNU tools like sed, awk/gawk, grep, things of that sort, in addition to the traditional tasks like running applications from the terminal, compiling source codes etc…

I reached a stage where I even listen to music from the terminal! Plus for some weird twisted reason, I reached a stage where I actually like (or enjoy) writing files in terminal editors like emacs or vi or vim. In any circumstance outside a PhD, I would seek immediately a psychological treatment 🙂

As an advice try to be on a regime of using Linux or whatever variation of POSIX for ALMOST ALL your tasks without exception in headless mode ONLY meaning just a terminal with a keyborad and screen with no GUI, no mouse/touchpad allowed! for months (!!). Can you do that? Of course you would need GUI for some tasks but trust me this will teach you tons of things that you can do. You will be frustrated to do  tasks which forces you to learn how to do them from the terminal. You would be amazed of how much you will learn by this torturing technique!

Some academics even require their PhD candidates to move to totally higher level in Linux like the folks working on implementing new network protocols at different level of the OSI layers stack or the ones who are working on the Kernel of Linux aiming to include a new feature. Usually here you should at a minimum level to be able to configure and build a kernel in Linux and then create your own Linux distribution from source code. Now some hypocrite academics make it sound more difficult then it actually is (remember what I said in the beginning of this article about the impression of knowledge as a way for these bugs to survive). By the way the majority of these hypocrites in Computer Science have web pages from the 1995 (so “smart in nonsense and writing fluff” and stupid enough to not know how to create a presentable simple responsive webpage in HTML5 and CSS3 which takes less than 20 minutes or even use a ready made WordPress template – How ironic!!!).

I advice you if you are interested to checkout the Linux from Scratch & the Beyond Linux From Scratch projects that teach you how to build up a Linux, layer by layer from source code. I am thinking of creating a series of articles and videos or even a Udemy course on this when I have free time, teaching how to build and compile a Linux distribution from scratch because I always believe the best way to learn is to teach.

In a nutshell, basic average Computer Science Joe needs to know Linux and Love it. That does not mean you should not be OS agnostic and love other operating systems such as Microsoft Windows and Mac OS or mobile operating systems.

If you happen that you want to work on Microsoft Windows or if your PhD needs this, I advises you to invest in learning PowerShell since it is a extremely powerful scripting language that do amazing things just like shell scripting (in bash, csh & tcsh, zsh etc..) on Linux.

Specific Linux skills I recommend to have:

1) Learn a terminal multiplexer and how to script it:  Most famous  are the GNU Screen command and tmux. Please checkout this article for more information. What is all the fuss about these tools? Why they are extremely important? They are super important since sometimes you have applications (servers ) that you want to script but you realise these applications themselves have their own terminals and their own commands that work with their terminals only. You ask yourself what the hell? How am I going to script those inner application specific commands and extract from these inner commands information to be used on the OS Level? Terminal multiplexers comes to the rescue. A good example I can give to elucidate this further is that of OpenSim, an open source multi-user virtual world. OpenSim is a server that host and render complex virtual worlds. It has its own commands and its own terminal/command line. I wrote an OpenSim – GNU Screen scripting article that I believe is very good on how to script OpenSim using a terminal multiplexer (I used in the article the GNU Screen command).

2) Learn how to write scripts with multi-threading like effect so you can run things in parallel when you do some capturing of metrics vel cetera. Please refer to an article I wrote on that.

The best way to learn Linux is by practice, so you can start by installing a virtualisation software (a.k.a hypervisors) such as VirtualBox (Free) on your host system, install and run the Linux distribution you like and begin learning. It is quite easy.

To get you started, there is a good series of  beginners tutorials online:  Linux Survival (Module 1, Module 2 & Module 3).

These are usually suggested to first year undergraduate students in the school of Computer Science at the University of St Andrews.

Great useful books on Linux that I have enjoyed reading cover to cover

Udemy courses

Jason Cannon courses are amazing. Buy them all if you can. The following are few courses I recommend:

Other instructors:

Practising Linux terminal commands Online:

Many tools allow you to write terminal commands and run them online from the comfort of your chair. These terminals are called Webminals or Web-based Shell Access Terminals. The following are few examples:

  • Webminal
  • Linuxzoo – “Learn Linux from the safety of your chair using a remote private Linux machine with root access”

Few tutorials online:

Skill 2 – Master Python OR Perl OR MS Windows PowerShell

I think I have mentioned PowerShell previously for the MS Windows folks (the equivalent of Bash Shell scripting for Linux folks). It is used a lot by Network and server administrators. If your PhD deals with Quality of Service or  System level performance measurements things of that sort. You need a good Language that helps you in your darkest days. But again you might say, Ok!!! PowerShell for Windows, Shell scripting for Linux! that is too much! Well there is are many rings that rule them all, the most famous are mentioned below:

The first ring is Python: the king of all kings, the king of all languages, the jack of all trades, the master of all masters 🙂 since this language is extremely versatile and can do anything under the Sun, yes anything (web development, scripting operating systems, machine learning, deep learning, data science and statistics, web scraping and list goes on and on till the end of time….).

Python is one of the easiest language to learn. Actually I discovered in UK that they actually teach their students how to program in Python in their schools (so you can know what environment are you coming to if you are coming from a 3rd world country). So the majority of students that start their degrees in universities already know Python!

Python allows you to write scripts exactly similar to Linux/Unix shell scripts to automate tasks of measurements and to capture metrics. The beauty of Python is that you can run the same script on Microsoft Windows, Mac OS and Linux or any other OS. Python is the language used  by default on your Raspberry Pi to program it to talk to sensors and actuators and is used  extensively in Internet of Things (IoT) applications and research.

Python and R are the languages of Machine Learning/ Deep learning these days. Python can be used in your data analysis, in statistics and allows you to draw the fanciest plots (data visualisation).

Python is used so much in hacking and security PhDs, haven’t  you watched the Mr Robot series?  Python is the most used language for creating quite easily hacking and security tools these days. A lot people are writing great hacking and security tools with Python and with utmost ease (that is the main difference from writing these tools with other ‘traditional’ languages). You can write yourself with modules like scapy , quite easily Network scanners, Man In the middle attacks ( ARP Spoofers, DNS Spoofers etc…), Packet sniffers, File Interceptors/File Injectors, JavaScript and HTML Code Injectors, credentials harvesters, enormous amount of malware (viruses, Trojan horses, backdoors…) and tools that protect against all these. Some of these tools are just literally few lines of code (I am not joking) and all the power is at your hand!!!

For agressive hacking, all you need is an OS such as Kali Linux , [plus] mastery in Python [plus] mastery in Computer Network Protocols and you can bring the biggest governments these days on their knees.

Black hat/grey hat hackers can buy from the dark & deep web many zero day viruses & python hacking tools undetectable by anti-viruses that actually work. You probably know that more than 90% of the web is hidden. They usually use a VPN software or the Invisible Internet Project anonymous network to avoid IP leakage and utilise powerful Linux distributions such as whonix, Tails, Qubes OS and Subgraph OS with a massive set of tools especially privacy-based web browsers such as the Tails darknet browser or Tor to access onion and clearnet websites.

Of course always aim to be an ethical hacker and aim always to help private companies, institutions and governments with your knowledge if you are doing research into security  and hacking because black hat & grey hat hacking are morally wrong (it is not different from robbing a bank or killing someone) before being illegal. Look at a concrete example, those despicable low scum bags who hacked the National Health Service (NHS) in UK,  and  by that hurting many patients who have heart attacks, who are old etc…! why? How inhuman and pure evil is this behaviour! So please understand that yes I want you to master Python but I do not advocate any unethical or illegal activities using it. I can understand if I try hard, people who hack the NSA or some corrupt governmental agencies in the aim of exposing any wrong doing (i.e. whistle blowing), but I disrespect completely people who commit crimes and make people loose money or loose their lives.

So you can see again why if I asked in the past, myself (with the knowledge that I have now), what language should I learn before even stepping into a PhD in Computer Science?  He will yell at me loud and clear, learn Python. Of course, if you are starting, you will learn Python 3. Actually this is irrelevant now since Python 2 will be dead very soon (thank goodness!!), no support at all for it (but you might still be using Python 2 for legacy code so you need to understand the differences). Finally! this  nonsense of Python 2 and Python 3 is coming to an end!!

When I started my PhD, I did not knew Python, we never used it. I programmed in the past with languages like C/C++, C# and VB (.NET),  JavaScript, Java and PHP but not Python 🙁 so when I came to UK and saw everybody using it, and I was terrified!

Terminal Multiplexers in Python: Python can do terminal multiplexing, recall when I talked about their importance and usage in the Linux (POSIX) skill section. You DO NOT need to leave Python toward shell scripting for this task, checkout as an example: pymux.

Disadvantage of Python: Python might be slow for some tasks especially if your doing some very complex tasks that requires high speed of execution or tasks that are very time sensitive. In this case, people usually use languages like Rust or Google Go or C/C++/C– sisters. Although, lately, to be frank, a lot of python modules have been optimised to run at C language speed. For more information, check these articles:  Why is Python so slow?, Why Python is Slow: Looking Under the Hood, and Yes, Python is Slow, and I Don’t Care.

Another language that is heavily used is Perl: Perl is also another “ring that rule them all” kind of language but in my opinion less than Python in prestige. It is ideal for scripting operating systems and is also cross-platform. One script for all of them.

I can see why network folks are attracted to it like a magnet. Perl is a very powerful language especially when it comes to parsing long texts for patterns. Perl supports object oriented, procedural and functional programming. Also a lot of folks are in love with the Perl language because of the good and many interfaces with C/C++ libraries. Perl used to be used extensively for server side web programming in the mid and late nineties but somehow died  in favour of cooler kids such as Python, PHP, JavaScript and Ruby (Ruby on Rails).

Few resources to just get you started with Perl & Python:

  • TutorialPoints – TutorialPoints is a great online website that provide guides to many technologies. They used to allow you to donwload the whole guide as a PDF now they require money and you can only download a small portion of the guide.  Search for Perl, they have a good introductory guide.
  • geeksforgeeks –  Another great website for learning major programming languages – This is just get you started

Few Perl Resources (just to get you started – not really advanced):

Skill 3 – Master writing complex Regular Expressions

I feel ashamed of suggesting that this is a skill needed for a PhD, because it is a skill needed for any junior programmer (it is an undergraduate level skill), but believe it or not, I saw many PhD candidates that I help out, struggle in this. It does not matter what programming language you are writing your code in, what matters really is that you should master writing Regular Expressions. I will give a concrete scenario. You are writing per instance Python scripts to capture traffic and different QoS networking parameters. You will probably use the Python subprocess module  a lot to call Linux commands (or whatever OS related commands for that matter) and receive back results or outputs. That output would be in the form normally of a long text with many goodies inside. So you need to sift and search all this text and retrieve exactly the small pieces that you want, so that you can use them in another process in your Python program or whatever. In this situation you need to master regular expressions.

There is an awesome website called Pythex that builds the regular expressions for you. It is also a good learning tool. You give it all the bulk text (normally the output of a Linux command that you want to extract (i.e. scrape) data from) and tell it what to do by choosing from a list of Regular Expressions cheatsheet. Then if you using Python, you probably need to use an awesome Python module called re (there are other fancier techniques and modules of course). Pythex website is not only for Python but also for any programming language under the Sun.

Skill 4 – Master Version Control Systems & Markdown

Version Control a.k.a as revision control or source control, call them whatever you like, they are essential for any Computer Scientist  or any software engineer or developer and Yes! even essential for a lonely wolf like a PhD Candidate. Version control systems are used to manage source code repositories in the development process, they allow software engineers and developers to track modifications in the software and synchronise those modifications between a large group of developers. In addition, you can develop multiple versions of the same software from the same code base. Version control/Revision control systems are not only for software development (meaning not only for your  JavaScript, C, C++ projects, vel cetera) but can also be applied to anything digital (yes!). They have been used in CAD (Computer Aided Design) systems, in 3D Models, in 3D Environments, in documents etc… The concept is there for everyone to use. Nevertheless, here I advise you to focus on the ones that deal with controlling and revisioning programming source code. Two famous kids on the bloc (there are others of course):

  1. Git version/revision control system
  2. Mercurial version/revision control system
  3. Apache Subversion
  4. GNU Project Bazaar

Have GitHub and Bitbucket accounts

As a PhD candidate, it is important that you have accounts on both GitHub and Bitbucket for storing all your software that you have developed during your PhD (unless if there is an embargo on them). Actually if you have been a programmer in anything really and especially if you have worked in a company before the PhD, you would have got accounts on these services already, right? Even during the process of applying for a programming job in a company, these repositories constitute the portfolio of the projects that you have worked on or have contributed to and showcase your abilities to employers. Furthermore, a lot of prospective PhD advisers now require their prospective PhD students to present a Github portfolio of all projects done, in advance of moving forward with a Research Proposal.

Moreover, it is important from a PhD perspective, to reference all those software/tools that you have created during your research in your Final Thesis. This gives the opportunity for your examiners to look at the code of your projects, if it is deemed necessary. In addition, it gives everyone else the ability to use your tools. Isn’t one of the main requirements of a PhD, an original contribution to knowledge in the discipline? I hate when I see in academic papers or PhD theses, authors saying: we did this, we did that! and then you say to yourself waw! that is awesome! so where we can see this awesome project. Then sadly, you realise you can not find anything.

Now you might be working on a very sensitive piece of software that could be military grade or could be commercialised and you want to put an embargo on your work. A lot of these version control services offer you many techniques to make your repositories private or password protected. Of course if you plan on doing this, you should give your examiners precise guidelines on how to access the repositories.

Master writing in Markdown Language

Every repository you create for your PhD project’s code or for any purpose for that matter, needs to have a very good well-written documentation. Markdown is a text to HTML formatting language used by many programs and services such as Github, stackoverflow among many others to facilitate the creation of fast beautiful HTML documentation.

I advise you strongly to learn Markdown, it is extremely easy. It won’t take from your time more than an hour to learn but makes a big difference.

Start with reading the tutorials on Github: About Writing on Github articles. You can check the GitHub Flavored Markdown Spec (it is a long specification document that you do not need to read  but it is good for checking how to do in Markdown something you need), then you can read Daring Fireball Markdown and Markdown guide.

In addition, have a look at how famous Github repositories have written their documentations.

Skill 5 – Don’t miss out on Computer Science Schools’ events/ Hackathons/ Game Jams/ Coding competitions and opportunities to teach

Hackathons\coding competitions: Don’t miss-out on important Computer Science events and coding competitions. I know you are a PhD, you want to sit in your office. That is not good. A ‘Hackathon‘ is also called ‘Hack Day’ or ‘Hackfest’ and if it dedicated for creating computer games it is called a ‘Game Jam‘. A hackathon and by extension a game jam is short period of time event (usually 24 to 72 hours although could span few days) which involves participants usually programmers, games designers, software engineers etc divided into teams that compete for prices. The aim is to produce a working prototype of a software, web site, game you name it in this short period of time (under the pressure of a deadline)

All prestigious schools of Computer Science in UK organise Hackathons and Game Jams every academic year. They are usually sponsored by big companies in the IT sector. When I was doing my undergraduate, we did not have such luxuries and incentives. Hackathons are very good places for your to participate as a PhD candidate either directly in a team, or sometimes the School wants volunteers to help organising the event. Don’t miss out on these stuff, they are not only for undergraduates but also for postgraduates. Participation will improve both your technical and transferable skills (problem solving skills, team work, presentation skills etc..). Also this is good thing to put on your CV (for both academic and non academic position).

A lot of prestigious CS conferences have a form or another of a diluted hackathon: It is a recent trend! A lot of conferences are having sessions that sometimes span the conference days and where researchers from industry and academia with accepted papers can participate and collaborate on specific projects during certain sessions whether designing or implementing them. They have to present them at a specific day at the end of the conference. In many cases, this might be in the form of a full day workshop in which participants are divided into teams to work on particular small projects. You, as a PhD candidate, should be proactive and participate in those sessions when you attend conferences.

Helping out in teaching and tutoring: Help out the school in teaching, demonstrating, tutoring and marking Computer Science modules. Teaching a course obliges you to master it (from my personal experience). “The best way to learn is to teach”. In addition, teaching experience is very good for your CV (even if you do not want to apply for a purely academia position by the way).

Other People good PhD skills recommendations

Computer Science Research and PhDs

  1. Recommendation by Toby Walsh – Computer Science and Engineering Department -UNSW Australia
  2. Recommendations by Prof Saleem Bhatti – School of Computer Science, University of St Andrews – UK
  3. Recommendations by Dr Tristan Henderson – School of Computer Science, University of St Andrews – UK

Other disciplines

  1. Recommendations by Prof Alex Wood – School of Psychological Sciences and Manchester Centre for Health Psychology, University of Manchester

Books to read

  1. How to write a thesis by Murray
  2. How To Get A Phd: A Handbook For Students And Their Supervisors by Estelle Phillips and Derek.S. Pugh
  3. The Unwritten Rules Of Phd Research by Marian Petre
  4. A manual for writers of research papers, theses, dissertations by Turabian

References

[1] Müller-Bloch, Christoph, and Johann Kranz. “A framework for rigorously identifying research gaps in qualitative literature reviews.” (2015).

[2] The UK Quality Assurance Agency – The UK doctorate: a guide for current and prospective doctoral candidates found at https://www.hope.ac.uk/media/studywithus/postgraduateresearch/documents/Doctorate-guide.pdf

[3] Saunders, Mark, Lewis Philip, Thornhill Adrian. Research methods for business students, 7/e. Pearson Education, 2016.

[4] Fong, Philip WL. “Reading a computer science research paper.” ACM SIGCSE Bulletin 41.2 (2009): 138-140.

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