Discrete math topics teach amazing way to think that most people never get to see in high school. Knowing stuff in free a book like [0] is immensely helpful. Th

Discrete math topics teach amazing way to think that most people never get to see in high school. Knowing stuff in free a book like [0] is immensely helpful. There are a ton of decent introductory discrete math books like the ones by Susanna Epp, Ed Scheinerman, Goranko bros and Gary Chartrand. Just google "list of discrete math book". What google spits out won't even scratch the surface of what's available out there. But modern CS folk will have to know much more than the basics of discrete math. For example, math analysis and probability theory are very helpful. This free book [1] gives a sampling of such topics. As preparation, one can start by looking at pre-real-analysis books like the ones by Lara Alcock and Jay Cummings, Linear Algebra by Kuldeep Singh and Probability Theory by Dimitri Bertsekas/Tsitsiklis. These books are very easy to read. There are also introductory books that give a bare-bones sampling of most undergrad math subjects from abstract algebra to topology like the ones by Gary Chartrand (separate from his discrete math book) and Steve Warner. Such books are designed to be as hand-holdy as possible. The more I type the more I realize there's more (much, much, much more) to say about the math side of things. Anyway, for another thing, google category theory just to be aware of it. There are a few undergrad/high school level books on the subject, but I am not sure how useful that is to a freshman. [0] Book Of Proof by Richard Hammack https://www.people.vcu.edu/~rhammack/BookOfProof/ https://www.people.vcu.edu/~rhammack/BookOfProof/ [1]Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March%202019.pdf https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March...