Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)

Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)

<P><STRONG>Probability and Statistics for Data Science: Math + R + Data</STRONG> covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously:</P> <P></P> <P>* Real datasets are used extensively. </P> <P></P> <P>* All data analysis is supported by R coding. </P> <P></P> <P>* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.</P> <P></P> <P>* Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture."</P> <P></P> <P>* Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner.</P> <P></P> <P>Prerequisites are calculus, some matrix algebra, and some experience in programming.</P> <P></P><B> <P>Norman Matloff</B> 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 <I>Journal of Statistical Software </I>and <I>The R Journal</I>. His book <I>Statistical Regression and Classification: From Linear Models to Machine Learning</I> was the recipient of the Ziegel Award for the best book reviewed in <I>Technometrics</I> in 2017. He is a recipient of his university's Distinguished Teaching Award.</P> <P></P> <P></P>