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Probability and Statistics for Data Science: Math + R + Data
Hardcover
Series: Chapman & Hall/CRC Data Science
Business GeneralDatabasesProbability & Statistics
ISBN13: 9780367260934
Publisher: Crc Pr Inc
Published: Jun 25 2019
Pages: 412
Weight: 1.70
Height: 1.10 Width: 6.20 Depth: 9.30
Language: English
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.
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