Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)
Language: English
Published by Chapman and Hall/CRC, 2019
- Softcover
- New
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- Title
- Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)
- Author
- Matloff, Norman
- Publisher
- Chapman and Hall/CRC
- Publication year
- 2019
- Condition
- New
- Binding
- paperback
- Language
- English
- ISBN 10
- 1138393290
- ISBN 13
- 9781138393295
- Item weight
- 19 ounces
- Dimensions
- 6x1x9
- Series
- Book 1 of 36: Chapman & Hall/CRC Data Science
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
Broad Street Books
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