Probability and Statistics for Data Science Math + R + Data by Norman S. Matloff

$47.99~~was$48.77~~Save 1%

Buy 2, get 1 free. Applied automatically at checkout.

Before you buy

Check your email at checkoutYour download link is sent to the address you enter, so make sure it's typed correctly.
This is a digital PDFNothing is shipped. The download link arrives by email right after payment.
30-day money-back guaranteeIf the file doesn't work, we'll replace it or refund you.

Can't find the ebook you want?

Send us a photo of the cover in live chat and we'll look for it. We usually reply within 10 minutes.

About this book

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.