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4 Books To Help You Learn Data Science Prerequisites
It wasn’t until I reluctantly picked a platform to learn data science that I realized that, rather than watching videos, learning online by reading and practicing is just as effective.
Before, what I had in mind was to learn entirely on my own, and the approach I took was to figure out data science prerequisites and then find ways to learn each individually. Being a text person, among the many avenues I resorted to for learning these prerequisites was to read books.
Per my findings, to get started with data science you need at least basic knowledge of:
Statistics:
- Bayesian Statistics
- Descriptive Statistics
Mathematics:
- Linear Algebra
- Calculus
Programming Language(s):
- Python/R/SQL
I found effective, beginner-level books for each of these. Here are four:
Bayesian Statistics
Think Bayes by Allen B. Downey
This is an intro to Bayesian statistics and the book’s premise is that if you can program, then that skill can be used to learn other topics. The book is a part of the Think X series.
