When I have a choice to take only one of two possible sequences: A theoretical one which aids my research and an applied one that aids my employability, which one should I opt for? (I must do the other on my own)


I am currently in a PhD program for Applied Math. I have finished all my core courses but am now required to take any (from a list) of courses which span Econ (Micro Econ, Time series type stuff), Management (Business Intelligence) and Statistics (Mathematical Stats, the gnarly stuff). My research is on Big Data Analytics from a Mathematical perspective.

I want to keep my options for engineering (Electrical/Industrial Engineering) and Business (Data Analytics) faculty positions open. I want to join academi and in my department, senior students have gotten jobs in all of the three quoted above.

From that standpoint, what courses should I be taking? My time is constrained and I have to choose one of the three. I know for my research, I will benefit from a more rigorous mathematical training in Stats but I don't know if this will help me get a business school job. If I choose one, I know I can (& will probably make time) do the other two on my own but I am confused which one to pick.

The way I think :

  • If I do stats, I will have a rigorous background in any kind of statistical inference which is gold for what I do but I will have no clue about microecon, finance or business intelligence. All three are big application areas for my research.

  • If I do econ/business intelligence, for my research, I might get stuck at times when I am trying to interchange an expectation with a gradient and wondering "under what conditions can I do this".

  • 1
    While the current version of this question, even the "global" version is too localized, I think there is a good general question, for which the answers would be helpful. Please consider editing the question and then voting to reopen.
    – StrongBad
    Apr 5, 2013 at 9:45
  • @DanielE.Shub, done. Hopefully, this one is more general.
    – user107
    Apr 5, 2013 at 17:02
  • 1
    The edit does make the question more general, which is why I voted to re-open, but I will warn that this question is not likely to yield useful answers. The obvious answer is "it depends on your goals", which you probably already know.
    – eykanal
    Apr 5, 2013 at 18:24
  • Is the stats class taught in an applied way? How theoretical is it?
    – cartonn
    Apr 5, 2013 at 19:18

1 Answer 1


The question is still very localized, but as somebody with Ph.D. in Statistics and MSc in Economics, working in industry after an unsuccessful start in academia, I would suggest taking the courses in the discipline you will be aiming at. I am not sure as to who is going to hire for Big Data Analytics; I rate statisticians as unlikely to do that (except probably for Carnegie Mellon and Stanford), and I am nearly certain that economists won't (they won't hire an Applied Math Ph.D. no matter how good you are), so business schools are your best shot -- some of them may hire people with wildly different backgrounds; UPenn has a Stat department housed in the business school, although that's a legacy issue rather than something deeply profound; Chicago has a small but strong statistical group; you might be able to find statistical groups in other places, too. Thus you would want to take Management courses in the mean time. An average business school faculty won't tell expectation from the gradient, except for a tiny fraction of quantitative finance people doing high frequency financial econometrics. My feeling is that an average Big Data architecture computer scientist/engineer won't tell an expectation from a gradient, either. (The only I thing I know about it is that at least one of the limits has to be uniform :) ). I may be entirely wrong with my assumptions about business schools, and if you don't see any Math/Appl Math/Stat Ph.D.s hired in the past 10 years in the top 30 schools that you've looked at, you don't have any chance, either, and should just announce your intention to go to industry, take all the classes you can, and apply to Google asking for a $3e5 starting salary ;).

The biggest career suggestion I could give is to move to Bay Area; I don't really know how to do Big Data if you are not there.

  • My feeling is that an average Big Data architecture computer scientist/engineer won't tell an expectation from a gradient, either. — Them's fightin' words!
    – JeffE
    Apr 6, 2013 at 1:38
  • A couple of things : I have a really strong math background and that is something I hold dearly and aim to exploit whenever I can, my research is more towards statistical machine learning. Anyways, why Bay Area?
    – user107
    Apr 6, 2013 at 1:57
  • lanyrd.com/topics/big-data/in/usa: California, 10 conferences; Bay Area, 6 conferences; rest of the US, 15 conferences. Are you sure you really know the landscape of this discipline?
    – StasK
    Apr 6, 2013 at 13:15
  • @StasK, what I do is actually statistical learning in the Chicago area. My advisor publishes a lot in optimization and control conferences rather than analytics/big data.
    – user107
    Apr 8, 2013 at 2:25

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