Getting started, algebra was enough for me. However, as I got slammed by algorithmic complexity problems like nested loops and needing to pick out the proper ty

Getting started, algebra was enough for me. However, as I got slammed by algorithmic complexity problems like nested loops and needing to pick out the proper type of collection (data structure/sorting) for holding information, things like Big-O notation and Discrete math became more interesting. Once I started doing work with financial analysis and other optimization problems, calculus became quite useful, even if it was just to calculate an occasional slope. When I wanted to understand the underpinnings of deep learning, linear algebra concepts like working with vectors and matrices as well as more calculus was quite handy. Now, I'm really interested in quantum computing and having only scratched the surface of linear algebra, I need to go back to the books to build a foundation to move forward on. I suppose it's an individual choice for the direction you want to go in.