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

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

$2,310.00

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內容描述

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.

作者介紹

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.