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Probability and Statistics for Data Science: Math + R + Data
(Chapman & Hall/CRC Data Science Series)
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business account Series: Chapman & Hall/CRC Data Science Series Paperback: 444 pages Publisher:
Chapman and Hall/CRC; 1 edition (June 20, 2019) Language: English ISBN-10: 1138393290 ISBN-13: 978-
1138393295 Product Dimensions: 6.1 x 1 x 9.2 inches
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Book 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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