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

This book 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.

 

 

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.