3273900Probability and Statistics for Data Sciencehttps://www.gandhi.com.mx/probability-and-statistics-for-data-science-9780429687112/phttps://gandhi.vtexassets.com/arquivos/ids/3442588/c02d44dc-193a-47f2-8e5d-a0bef0de0fa1.jpg?v=63838538033267000017431743MXNCRC PressInStock/Ebooks/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 universitys Distinguished Teaching Award....3210000Probability and Statistics for Data Science17431743https://www.gandhi.com.mx/probability-and-statistics-for-data-science-9780429687112/phttps://gandhi.vtexassets.com/arquivos/ids/3442588/c02d44dc-193a-47f2-8e5d-a0bef0de0fa1.jpg?v=638385380332670000InStockMXN99999DIEbook20199780429687112_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9780429687112_Probability and Statistics for Data Science: Math R Data covers math statdistributions, 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 universitys Distinguished Teaching Award....(*_*)9780429687112_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 universitys Distinguished Teaching Award....9780429687112_CRC Presslibro_electonico_c726c8fa-7835-3cd7-88b3-96811588291d_9780429687112;9780429687112_9780429687112Norman MatloffInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/taylorandfrancis-epub-ba30fa1c-c8c0-433d-9393-ca646370d38c.epub2019-06-21T00:00:00+00:00CRC Press