Math checklists for Data Science?

Question

data mining

I decided to upgrade my mathematical background for data science.
Highlighted the following areas:

1. Discrete Math
   
2. graph theory
   
3. Theory of Algorithms
   
4. Statistics
   
5. Probability theory (!!! not one probability, but many probabilities)
   

(quite right, probabilities, I just illiterately translated the English version of Probability Theory)
help me make a checklist for each area, those things, concepts that need to be accurately learned in the context of practical applicability for data science and those that are not very practical.
Type:
Graph theory - Dijkstra's algorithm, in discrete mathematics - you should not go deep into mathematical logic, etc.
+ (but this is space, I understand) practical criteria by which this knowledge can be assessed (any problems)

Answer 1

How to learn to write data processing, machine learning and where to go with it? - here are some good articles :)