Probability and Statistics for Data Science: Math + R + Data 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 theJournal of Statistical SoftwareandThe R Journal. His bookStatistical Regression and Classification: From Linear Models to Machine Learningwas the recipient of the Ziegel Award for the best book reviewed inTechnometricsin 2017. He is a recipient of his university's Distinguished Teaching Award. | Probability And Statistics For Data Science by Norman Matloff, Paperback | Indigo Chapters