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Probability and Statistics for Data Science
Av Norman Matloff, 2019.Del av serien Chapman & Hall/CRC Data Science Series.
Math + R + Data
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 Technometricsin 2017. He is a recipient of his university's Distinguished Teaching Award.
Språk: Engelsk
Butikkpris: 929,-
848,-
Ikke tilgjengelig for Klikk&Hent
Midlertidig tomt på lager
Bestillingsvare. Forventes sendt om ca 10 dager
Oppdag mer
Probability and Statistics for Data Science
Av Norman Matloff, 2019.Del av serien Chapman & Hall/CRC Data Science Series.
Math + R + Data
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 Technometricsin 2017. He is a recipient of his university's Distinguished Teaching Award.
Oppdag mer
Butikkpris: 929,-
848,-
Språk: Engelsk
Ikke tilgjengelig for Klikk&Hent
Midlertidig tomt på lager
Bestillingsvare. Forventes sendt om ca 10 dager
848,-
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