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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.
Produkteigenschaften
- Artikelnummer: 9781138393295
- Medium: Buch
- ISBN: 978-1-138-39329-5
- Verlag: Taylor & Francis Ltd
- Erscheinungstermin: 20.06.2019
- Sprache(n): Englisch
- Auflage: 1. Auflage 2019
- Serie: Chapman & Hall/CRC Data Science Series
- Produktform: Kartoniert
- Gewicht: 670 g
- Seiten: 444
- Format (B x H x T): 154 x 231 x 30 mm
- Ausgabetyp: Kein, Unbekannt