Data Science and Machine Learning: Mathematical and Statistical Methods

Gold-standard introduction to statistical learning covering regression, classification, resampling, regularization, tree-based methods, SVMs, deep learning, and unsupervised learning with Python labs.

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor

Rigorous treatment of probability and statistics tailored for data science from NYU Courant Institute. Covers probability, estimation, hypothesis testing, linear regression, and PCA with R implementation.

Carlos Fernandez-Granda