Matloff Norman
Matloff Norman - Probability And Statistics For Data Science Math + R + Data - Paperback
Matloff Norman - Probability And Statistics For Data Science Math + R + Data - Paperback
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Binding: Paperback
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 analysis is supported by R coding. * Includes many Data Science applications such as PC a 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.
Title: Probability And Statistics For Data Science Math + R + Data
Author(s): Matloff Norman
Publisher: Taylor & Francis Ltd
Barcode: 9781138393295
Pages: 412 Pages
Publication Date: 6/20/2019
Series: Chapman & Hall/CRC Data Science Series
Category: Probability & Statistics
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 analysis is supported by R coding. * Includes many Data Science applications such as PC a 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.
Title: Probability And Statistics For Data Science Math + R + Data
Author(s): Matloff Norman
Publisher: Taylor & Francis Ltd
Barcode: 9781138393295
Pages: 412 Pages
Publication Date: 6/20/2019
Series: Chapman & Hall/CRC Data Science Series
Category: Probability & Statistics
