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 Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award. |Probability and Statistics for Data Science Math + R + Data | Statistics
