I like the new Data Science term, because it suggests people might be more open to paying money for math. Coming from an operations research background, I know

I like the new Data Science term, because it suggests people might be more open to paying money for math. Coming from an operations research background, I know it can be a challenge to convince people that this is worthwhile. But really, the term to me more or less means "mathematically literate." I know, there are some techniques that seem to be specifically associated with data science, like the analysis of large scale datasets, but many engineering and mathematical disciplines do deal with this already. There's a reason these jobs want someone who has a degree in... math, statistics, computer science, operations research, physics, engineering, hell, let's just say "or related field" and be done with it. It's partly because these fields contribute to intersection called "data science", but my real guess is that a degree in any of these fields means that you're probably mathematically literate. You've done one of those majors that requires you to take calculus of several variables, linear algebra, some differential equations, some kind of probability and statistics, probably write a computer program or two, and then focus on some more specific branch in depth where you learn to model things mathematically. A good humanities curriculum will impart knowledge, sure, but it also trains you to read dense material, make sense of it, and express some kind of insight about it. A good "stem" curriculum does the same thing, except with numbers and data. There was a time when someone could get a job by being highly literate. I see this as a similar situation - if you're mathematically literate at a reasonably high level, you're probably employable.