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Probability and Statistics for Data Science : Math + R + Data, Hardback Book

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

Part of the Chapman & Hall/CRC Data Science Series series


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.


  • Format: Hardback
  • Pages: 412 pages
  • Publisher: Taylor & Francis Ltd
  • Publication Date:
  • Category: Probability & statistics
  • ISBN: 9780367260934

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