{"product_id":"probability-and-statistics-for-data-science-math-r-data-9781138393295","title":"Probability and Statistics for Data Science: Math + R + Data","description":"\u003cp\u003e\u003cstrong\u003eProbability and Statistics for Data Science: Math ] R + Data\u003c\/strong\u003e covers \"math stat\"-distributions, expected value, estimation etc.-but takes the phrase \"Data Science\" in the title quite seriously: \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e* Real datasets are used extensively. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e* All data analysis is supported by R coding. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e* Leads the student to think critically about the \"how\" and \"why\" of statistics, and to \"see the big picture.\"\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e* Not \"theorem\/proof\"-oriented, but concepts and models are stated in a mathematically precise manner.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003ePrerequisites are calculus, some matrix algebra, and some experience in programming.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003e \u003c\/b\u003e\u003cp\u003eNorman 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 \u003ci\u003eJournal of Statistical Software \u003c\/i\u003eand \u003ci\u003eThe R Journal\u003c\/i\u003e. His book \u003ci\u003eStatistical Regression and Classification: From Linear Models to Machine Learning\u003c\/i\u003e was the recipient of the Ziegel Award for the best book reviewed in \u003ci\u003eTechnometrics\u003c\/i\u003e in 2017. He is a recipient of his university's Distinguished Teaching Award.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eNorman Matloff\u003c\/strong\u003e 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 \u003ci\u003eJournal of Statistical Software \u003c\/i\u003eand \u003ci\u003eThe R Journal\u003c\/i\u003e. His book \u003ci\u003eStatistical Regression and Classification: From Linear Models to Machine Learning\u003c\/i\u003e was the recipient of the Ziegel Award for the best book reviewed in \u003ci\u003eTechnometrics\u003c\/i\u003e in 2017. He is a recipient of his university's Distinguished Teaching Award.\u003c\/p\u003e\u003cbr\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":50332587360530,"sku":"9781138393295","price":89.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_c386b529-0f84-448e-a9d2-69d2e10f1f08.jpg?v=1727840282","url":"https:\/\/surprise-castle.myshopify.com\/products\/probability-and-statistics-for-data-science-math-r-data-9781138393295","provider":"Surprise Castle","version":"1.0","type":"link"}