Rigdon S Introduction to Probability and Statistics for Data Science with R 2025
Rigdon S Introduction to Probability and Statistics for Data Science with R 2025 | 90.54 MB
Title: Introduction to Probability and Statistics for Data Science: with R
Author: Rigdon, Steven E., Fricker Jr, Ronald D., Montgomery, Douglas C.
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.
DOWNLOAD:
https://rapidgator.net/file/a5b097124d54a204da8a3b514f2b1423/Rigdon_S.Introduction_to_Probability_and_Statistics_for_Data_Science_with_R_2025.rar
https://nitroflare.com/view/D7D7B35D64B5B58/Rigdon_S.Introduction_to_Probability_and_Statistics_for_Data_Science_with_R_2025.rar
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.
DOWNLOAD:
https://rapidgator.net/file/a5b097124d54a204da8a3b514f2b1423/Rigdon_S.Introduction_to_Probability_and_Statistics_for_Data_Science_with_R_2025.rar
https://nitroflare.com/view/D7D7B35D64B5B58/Rigdon_S.Introduction_to_Probability_and_Statistics_for_Data_Science_with_R_2025.rar
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