Analytics is actually very cave-man level math and statistics. You could just work through Strang for linear algebra and a stats book and you'll know more than

Analytics is actually very cave-man level math and statistics. You could just work through Strang for linear algebra and a stats book and you'll know more than 80% of the people employed doing this work...and that's probably not even necessary. I will warn you that if you're already an infrastructure programmer, most of what analytics teams really need is someone to babysit the data. If you don't want to deal with that, I would disclaim all knowledge of computer programming beyond Microsoft Excel. There's loads of blogs where people discuss statistics/ML and echo Hal Varian's quote that it's the next "sexy career" but if you look at the authors of those blogs they are all still in grad school, so take that advice with a grain of salt. Actuaries have been employed doing stats for centuries and they have never been "sexy," but they get paid ok. Personally, I find statistics heinously boring so the excitement is lost on me. Instead of thinking "wow, we're awash in data, finally a reason to use my Stats 101" I tend to think: "oh fuck now that there's all this data I better not let anyone know I was an applied math major...I'll get stuck doing Stats 101 all over again." Now if someone wanted to pay me $200K a year to do knot theory, I'd be stoked... Graphics is math heavy but you learn that math in a computer science grad degree, not a math grad degree. Aside from Wall Street (where the useful math is often from the EE department, anyway) most math heavy programming jobs (i.e. "scientific computation") probably pay worse than what you're making now. Signed, your friendly neighborhood math teacher turned programmer.