Probability and Statistics for Data Science Math + R + Data (Paperback - 2020)
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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.
ISBN13 | 9781138393295 |
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Product Name | Probability and Statistics for Data Science Math + R + Data (Paperback - 2020) |
Price | ₹5,950.00 |
Original Price | GBP 56.99 |
Author | Matloff |
Publisher | CRC Press/Chapman Hall |
Publication Year | 2020 |
Subject | Mathematics and Statistics |
Binding | Paperback |
Language | English |
Pages | 376 |
Weight | 0.678000 |
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