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- Payer en toute sécurité
- Toujours un magasin près de chez vous
119,45 €
+ 238 points
Format
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.Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 412
- Langue:
- Anglais
- Collection :
Caractéristiques
- EAN:
- 9781138393295
- Date de parution :
- 20-06-19
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 155 mm x 231 mm
- Poids :
- 635 g
