Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)

Matloff, Norman

ISBN 10: 036726093X / ISBN 13: 9780367260934

Editore: Chapman and Hall/CRC, 2019

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Dati bibliografici

• Titolo: Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 036726093X
• ISBN 13: 9780367260934
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.