Probability and Statistics for Data Science: Math + R + Data

di Norman Matloff (Autore)

Taylor & Francis Ltd, 2019

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.

• Autore: Norman Matloff
• Editore: Taylor & Francis Ltd
• Collana: Chapman & Hall/CRC Data Science Series
• Anno: 2019
• Rilegatura: Hardback
• Pagine: 412 p.
• Dimensioni: 229 x 152 mm
• Peso: 848 gr.
• EAN: 9780367260934

Testo in English