I am a recent MS in CS grad with an emphasis in Data Science and I have decided I want to learn college-level math so I may apply it to specialized DS/CS problems in the future (optimization algorithms, NN architecture) and better understand DS procedures. I know a very small amount of calculus as it applies to gradient descent and stats/ML models. While I have the time and resources (I am currently unemployed and living with my parents), I have decided to take Single/Double Var Calc, Differential Eqs, and Linalg from MIT open course-ware. I have a couple questions..
(1) where in my journey will I learn about Fourier Transformations and Wavelet Transforms? I am reviewing them now for DS and was wondering where they will come along.
(2) Is there any other math classes I should plan on taking to succeed in a DS/CS career? I also have an interest in finance, and plan on taking a quantitative finance from MIT after these courses..
(3) Is there any math in particular (past these pre-reqs for the QF class) which would fall in line with the typical track for a quantitative analyst?
I appreciate any response– I am just trying to gauge the breadth of mathematics that will be of practical use to me in the future. I know these are rather open ended questions– an answer to the first will suffice. Thank you!