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

## Book 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 university's Distinguished Teaching Award.

## Book Details

- Title: Probability and Statistics for Data Science: Math + R + Data
- Author: Norman Matloff
- Length: 444 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2019-06-25
- ISBN-10: 036726093X
- ISBN-13: 9780367260934

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