Taking courses as a PhD student
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.