Foundations of Statistics For Data Scientists

Shows the elements of statistical science that are highly relevant for students who plan to become data scientists less emphasis on probability theory and methods of probability such as combinatorics, derivations of probability distributions of transformations of random variables (except for explanations of t, chi-squared, and F constructions) Formal statements and proofs of theorems, and decision theory Introduces some modern topics that do not normally appear in "math stat" texts but are especially relevant for data scientists, such as generalized linear models for non-normal responses (e.g., logistic regression) Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python)