> what factors influence student performance, or to visualize perchasing trends, or to predict sales numbers so that the company doesn't over purchase perishabl

> what factors influence student performance, or to visualize perchasing trends, or to predict sales numbers so that the company doesn't over purchase perishables "Big data"? Those are all just everyday, common, ordinary, vanilla-pure, old-fashioned exercises in statistics with maybe a little optimization thrown in, that is, the 'mathematical sciences' or, if you will, with a lot of overlap of 'operations research'. But long the standard remarks were that "operations research is dead" and "statistics died long ago except for some of biomedical statistics", e.g., for doing FDA approved 'trials'. Generally the amount of relevant data available and needed for such work is not very 'big'; yes, maybe the relevant data comes from a database of 10 TB, but a simple SQL query should be able to extract into, say, just a simple 'flat' file, maybe comma separated values or name-type-value triples, what is relevant. From the OP > 4. We need 10x or 100x more data scientists and AI specialists Again not a prediction. We are already seeing this. So perhaps I can sharpen some of my old skills in computer vision. Ah, come ON! So, all of a sudden college courses in undergraduate, junior level in mathematical statistics -- weak/strong laws of large numbers, Lindeberg-Feller central limit theorem, Neyman-Pearson lemma, Cramer-Rao lower bound, minimum variance, unbiased estimation, confidence intervals, hypothesis testing, experimental design and analysis of variance, etc. are packed? I doubt it! And course in optimization -- unconstrained sufficient conditions, roles for convexity, Kuhn-Tucker conditions, the simplex algorithm, Lagrange multipliers and Lagrangian relaxation, nonlinear duality, the simplex algorithm and how to use it to solve nonlinear/integer problems, integer linear programming, etc. -- is also getting going, say, if only for maximum likelihood estimation in 'machine learning'? Why do I doubt it? Guys, yes, for much of anything practical in statistics or optimization will require computing and software to do the data manipulations. Alas, that does not mean that .NET or computer science is what is central to the work. I may be that all that is needed from computing is SAS, R, some open source statistics/optimization code, etc. .NET has a big role in computing, but it's not intended for statistics/optimization. Who the heck put the $10 into the hype machine?