Probability and statistics for data science: math + r + data, paperback/norman matloff

Description 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 universitys Distinguished Teaching Award.

About the Author 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 universitys Distinguished Teaching Award.