<span class="a-text-bold">Probability and Statistics for Data Science: Math + R + Data</span> covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously:

* Real datasets are used extensively.

* All data analysis is supported by R coding.

* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.

* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."

* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.

Prerequisites are calculus, some matrix algebra, and some experience in programming.

Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the <span class="a-text-italic">Journal of Statistical Software </span>and <span class="a-text-italic">The R Journal</span>. His book <span class="a-text-italic">Statistical Regression and Classification: From Linear Models to Machine Learning</span> was the recipient of the Ziegel Award for the best book reviewed in <span class="a-text-italic">Technometrics</span> in 2017. He is a recipient of his university's Distinguished Teaching Award.