The holy trinity of data science fundamentals is linear algebra, statistics, and Python programming. After learning the basics, the best method of attaining mastery in the fundamentals is progressively more difficult projects. Along the way, recognizing gaps in your knowledge and filling those gaps.
Here are a handful of suggestions to guide you on the path:
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Linear Algebra:
- A 2020 Vision of Linear Algebra by Gilbert Strang
- Linear Algebra Done Right by Sheldon Axler
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Statistics
- Regression and Other Stories The first couple chapters are exquisite
- Statistical Rethinking by Richard McElreath
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Python Programming
- Python for Everybody Complete the exercises which are autograded.
- Repl.it has complete courses with autograded exercises Intro & Intermediate