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Question for recent and current PhDs: which skills are most useful in the job market?


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Hi all!


As a current applicant, I want to make sure that I am 'smart' about identifying a program that is right for me. With this being said, I am seeking out input into which skills make psych PhDs more marketable for professor, industry/government, and nonprofit jobs (since there's no predicting what the market will look like in 5-6 years, I don't want to narrow this down to a single sector).


So, offhand (and hopefully this will get some creative juices flowing for folks that are willing to help me, and hopefully other applicants that see this message, out):

1) Tech skills: Which programs should I inquire about? How important are programming skills? Which programming languages? 

2) Stat skills (also related): Is it worthy to seek as much advanced statistical training as possible? If so, which techniques should I hone in on? For example, I perk up when I see opportunities to learn SEM.

3) Project management/project planning: Is this common to learn in graduate school, or is it so rare that I should I forget about even inquiring about it?


I will appreciate it if anybody could either (a) add to the existing points, or (B) add new points. 

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Stats & tech skills will often come hand in hand. There isn't a single sector which is without the need of data analysis. Specifically, someone competent who can extrapolate meaningful information from data.

It is my realization that just about all "professions" or "work like" environments need to do one thing (of course, not just this one thing). That is, they need to sell something and data analysis provides the selling points. 

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Matlab and R are useful to learn for science. Lots of imaging and analysis software rests on Matlab, and the Quant Psych thread indicated that the field is moving to R for statistical analysis. On top of that, learning object-oriented languages like Python and Java and maybe C/C++ may not come in handy directly for your career, but they certainly teach you general programming skills that Teach you to think about other coding projects and to learn new languages faster. (Plus it's nice to code your own calculator that carries across uncertainty values, haha). So for coding, really just pick a language and start working on it and you'll only increase your coding skills. Then the next year pick a second, and so on.

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R is where a lot of the interesting research is happening in and out of academia. There is nothing better than publishing a paper and a package side by side. But there is huge demand for Data Scientists in the overall job market which often calls for Python (or other programs beyond analyst-focused programs). But then again many jobs have operational code written in an older language, like SAS.


What I have found is that a specific language is less important than being really good at one langagure (your bread and butter) and an ability to learn new languages as needed. The first language you learn is the hardest, so pick one and get it down. Then pick up others as the situation requires. As a counter-example, it would be silly to only know a "hello world" program in 30 different langauges.


More than anything, being able to "teach" stats is very important. If you are in a position where you are the most stats heavy person, you have to be able to communicate to your boss and your bosses boss why these advanced statistical techiniques are important for their given problem.

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Follow up question: do non-quant programs tend to present opportunities for students to learn Python, Java, C/C++, Matlab, etc? 



I know R.... but not from academia. 

Edited by TheMercySeat
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That probably depends on your program. Some emphasize the ability to take classes or collaborate outside of the department (there's your opportunity to learn CS at the school), and some don't. But also, if you're self motivated, there are sooooo many online classes for teaching coding like coursera, udacity, and coding-specific websites. There's almost too many resources.

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Many (most?) PhD programs are fairly flexible, so you'd probably have the opportunity to take a class in Python, Java, or R if you wanted to.  Might be at the undergrad level.


I agree that stats is important (I'm in a stats & methodology postdoc) but I wouldn't spend too much time trying to learn every package, programming language and technique on the face of the planet.  Get a really good grasp of the basics and pick a signature area.  The reason is because if you know your theoretical stuff, you can always learn new statistical techniques later.  The underlying principles don't really change.  All statistical packages do relatively the same thing - a correlation is a correlation regardless of whether you do it in R or Stata or SPSS.  (I'm talking about all-purpose ones, not special focus ones.)  What changes is how you do it.  Once you learn one software package it's easier to learn another one, so if you can learn one or two very solidly in graduate school and have an interest in learning others, you should be fine.


And which one you learn really depends on what you want to do.  I only have a basic knowledge of R and plan to teach it to myself this year, and yes, R is what the statisticians and computer scientists are using.  But most of the non-academic social science type jobs (think tanks, NGOs, nonprofits, etc.) I've been looking at want SAS, SPSS, and/or Stata.  Most of them don't expect a programming language.  That's quite useful if you want to go into data science or tech.  But even at the tech firms that hire, for example, UX researchers - they don't expect you to know Python.  They want a social scientist who can do social science work.  (I'm not saying don't learn it - I think you should!  Because it's fun and useful, lol.)


The same is true of statistical techniques.  Personally I have found intensive longitudinal data analysis methods (multilevel modeling, time-varying effects, survival models, etc.), mixture modeling (LCA et al) and SEM to be most useful in my own work, but that's because of the nature of what I do.  For you it could be time series, data mining, or whatever.  Causal inference is another area that I have less interest in but could be useful or interesting to you.  Dynamical systems is a term that gets thrown around a lot where I am, lol.


Project management is hugely important, especially grants management.  A lot of non-academic places still compete for grants and government contracts.  You can learn it in grad school but like most things, you need to specifically seek it out.  Find a mentor (informal or formal) who has grants and sit in on how they manage it.  When they write the next one, ask if you can help.  Perhaps in your third year, think about writing your own - maybe an F31 or R36 if you do NIH-fundable research, or maybe an NSF dissertation grant.  postdocs are great for this, too, as they generally encourage you to learn about grantsmanship.


Writing is, of course, important - but it's often considered a "soft skill" and employers often don't realize that they are looking for it until they find someone who doesn't have it.

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