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Probability and Statistics for Data Science

Probability and Statistics for Data Science

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About the Book

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.


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Product Details
  • ISBN-13: 9780367260934
  • Publisher: CRC Press
  • Publisher Imprint: Crc Press
  • Height: 236 mm
  • No of Pages: 412
  • Series Title: Chapman & Hall/CRC Data Science
  • Sub Title: Math + R + Data
  • Width: 157 mm
  • ISBN-10: 036726093X
  • Publisher Date: 25 Jun 2019
  • Binding: Hardback
  • Language: English
  • Returnable: N
  • Spine Width: 28 mm
  • Weight: 772 gr


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