Matloff, Norman Author
Biography not available.
Browse all ART CRITICISM books
Browse all Ashworth books
Browse all BUSINESS AND ECONOMICS books
Browse all FOLKLORE books
Browse all GARDENS books
Browse all GRAPHIC NOVELS books
Browse all HISTORY - MEDIEVAL books
Browse all HUMOUR books
Browse all Juvenile Fiction : Biographical - Other books
Browse all Juvenile Fiction : Historical - Other books
Browse all Juvenile Fiction : Legends, Myths, & Fables - Other books
Browse all Juvenile Fiction : People & Places - Other books
Browse all Juvenile Fiction : People & Places - United States - Other books
Browse all Juvenile Fiction : Religious - Other books
Browse all Juvenile Nonfiction : History - Other books
Browse all Juvenile Nonfiction : Mathematics - Advanced books
Browse all Juvenile Nonfiction : People & Places - Other books
Browse all Juvenile Nonfiction : People & Places - United States - Othe books
Browse all KINDRED books
Browse all LITERARY STUDIES books
Browse all LONDON books
Browse all Lozada books
Browse all Medical : Nursing - Mental Health books
Browse all MIND books
Browse all MUSEOLOGY books
Browse all NON-CLASSIFIABLE books
Browse all NONLINEAR SCIENCE books
Browse all POPULAR SCIENCE books
Browse all RELIGIOUS STUDIES books
Browse all SPIRIT books
Browse all SPORT books
Browse all TRAVEL WRITING books
Out of Stock
Hardback ยท ISBN 9780367260934
Inclusive of GST and other taxes
Arriving at your doorstep in 10-15 days
Our logisitcs team will make sure everyitem is delivered safely
| ISBN | 9780367260934 |
|---|---|
| Publisher | Chapman and Hall/CRC |
| Publication date | June 2019 |
| Language | English |
| MRP | ยฃ187.99 GBP |
| Page count | 446 |
| Item weight | 168 g |
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.
Log in to write a review for this book.
No reviews yet. Be the first to share your opinion.
Biography not available.