Description: 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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EAN: 9781138393295
UPC: 9781138393295
ISBN: 9781138393295
MPN: N/A
Book Title: Probability and Statistics for Data Science: Math
Item Length: 23.1 cm
Item Height: 234 mm
Item Width: 156 mm
Author: Norman Matloff
Publication Name: Probability and Statistics for Data Science: Math + R + Data
Format: Paperback
Language: English
Publisher: Taylor & Francis Ltd
Subject: Mathematics
Publication Year: 2019
Type: Textbook
Item Weight: 603 g
Number of Pages: 412 Pages
