Hey math learners. In September this year I'll be beginning a study in Applied Data Science. The *applied* part is crucial, see below for more, but in short its not a computer science degree but more a "research methods" for social science and life science type of program.

Any Data Science majors, Masters or PhD's here who can guide me to what math concepts I need to prep?

Pls help. I'm relatively analytical but definitely need to refresh/acquire some basics. Booleans and logs still give me headaches and if I see equations with several roots and divisions I panic.

Background: I did fairly complex but definitely not advanced math in high school (I was a history/literature major, I remember relatively basic geometry and with algebra the most advanced I think we got was differentials) and did a refresher for bachelors (basically just a course on "Mathematicl Reasoning" and how models will look based on the equation type i.e. interpreting graphs), after which I did stats 100, 200 and 300 (up to multivariate regression and clustering). However, I mostly don't remember shit. The 300 level stats was 4 years agoo, sooo I need to start with the basics.

Why the applied bit matters: The program consists of an intro course that goes up until semi-advanced machine learning and clustering in terms of complexity and from then forward everything is basically research methods courses from different fields (media studies, behavioral science, geoscience, health science). I'll be doing the following courses: Critical Data Mining of Media Culture (mostly just ML model on text/images), Recommender Systems for Public Media (algortihm stuffs I gues), Causality in Social Science (idk what to expect here, big cumbersome multivariates with a lot of time I think) and Social Network Analysis (yay "network math (?)").