Materials for the book 'Probability and Statistics for Data Science' include a free preprint, 103 Python notebooks using 23 real-world datasets, 118 videos with slides, and solutions to 200 exercises.
The book covers probability theory and statistics, focusing on topics like random variables, models, correlation, estimation, hypothesis testing, principal component analysis, and regression/classification methods, using real-world datasets to address challenges like overfitting and causal inference.
Authored by Carlos Fernandez-Granda, an Associate Professor with 10 years of teaching experience, who provides learning advice and acknowledges contributors for typos and support from the National Science Foundation.