Probability and Statistics for Data Science: Math + R + Data

By Norman Matloff

Book Overview

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.

• Format: Hardcover
• Language: English
• ISBN: 036726093X
• ISBN13: 9780367260934
• Release Date: June 2019
• Publisher: CRC Press
• Length: 412 Pages
• Weight: 1.70 lbs.
• Dimensions: 1.1" x 6.2" x 9.3"