Probability and Statistics for Data Science : Math + R + Data Hardback
Part of the Chapman & Hall/CRC Data Science Series series
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.
- Format: Hardback
- Pages: 412 pages
- Publisher: Taylor & Francis Ltd
- Publication Date: 25/06/2019
- Category: Probability & statistics
- ISBN: 9780367260934
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