Probability and Statistics for Data Science

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 the Journal of Statistical Software and The R Journal. His book Statistical