These are good suggestions, and you're presenting a good guide for people who need to fill in some gaps prior to "grad school". But to me, this sounds like th

These are good suggestions, and you're presenting a good guide for people who need to fill in some gaps prior to "grad school". But to me, this sounds like the standard (in the US) undergraduate curriculum for the general STEM majors - Math, Physics, Operations Research, CS, various branches of engineering. Typically, a student would have done what you're describing prior to grad school. A Masters degree is typically an opportunity to specialize a bit and have a little more space to pursue an interest. One suggestion I'd make is to go through the python scikit-learn library, do the examples, and research the math behind the algorithms to make sure you understand what's happening in the background (maybe even implement a bit on your own). I would agree that if you have the background you described, you'll be able to do this (though it'll take some study). http://scikit-learn.org/stable/ http://scikit-learn.org/stable/ One other thing… I'm a little less enthusiastic about learning purely on a "need-to-know" basis, because sometimes it takes knowledge of an algorithm to recognize the opportunity to apply it. I don't think you can keep all of this loaded in "exam-ready" memory (and I really wish interviewers would understand this), but I think it's important to study and read widely. You can learn (or review) the details in the moment, but you do need broad awareness to approach data with a certain frame of mind.