Probability and Statistics for Data Science: Math + R + Data

by Norman Matloff

Probability and Statistics for Data Math + R + Data covers math stat--distributions, expected value, estimation etc.--but takes the phrase Data Science in the title quite * 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 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.

Genres: Mathematics

412 pages, Kindle Edition

Published June 21, 2019