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Probability and Statistics for Data Science : Math + R + Data.
・ISBN 978-1-138-39329-5 paper GB£ 66.99
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| 著者・編者 | Matloff, Norman, |
|---|---|
| シリーズ | Chapman & Hall/CRC Data Science Series |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2019 |
| ページ数 | 412 pp. |
| 言語 | ENG |
| ニュース番号 | <M25-5234> |
解説
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.