{ "item_title" : "Probability and Statistics for Data Science", "item_author" : [" Norman Matloff "], "item_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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/13/839/329/1138393290_b.jpg", "price_data" : { "retail_price" : "89.99", "online_price" : "89.99", "our_price" : "89.99", "club_price" : "89.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

Overview

Customers Also Bought

Details