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 is supported by R coding.
  • Includes many Data Science applications, such as PCA, mixture distributions, random 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 , and some experience in .

Norman Matloff is a professor of 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:
  • Length: 444 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2019-06-25
  • ISBN-10: 036726093X
  • ISBN-13: 9780367260934

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