Norman Matloff

Chapman and Hall/CRC

June 25, 2019
Forthcoming

Textbook
- 376 Pages

ISBN 9781138393295 - CAT# K400286

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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.

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