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Probability and statistics for data science: math + R + data

By: Matloff, Norman.
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC data science. Publisher: Boca Raton CRC Press 2020Description: xxxii, 412 p. Includes illustrations, bibliography and index.ISBN: 9781138393295.Subject(s): Mathematical statistics | Mathematical statistics - Data processing | Probabilities - Data processing | R (Computer program language)DDC classification: 519.5 Summary: 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. https://www.routledge.com/Probability-and-Statistics-for-Data-Science-Math--R--Data/Matloff/p/book/9781138393295
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Table of Contents

Basic Probability Models
Monte Carlo Simulation
Discrete Random Variables: Expected Value
Discrete Random Variables: Variance
Discrete Parametric Distribution Families
Introduction to Discrete Markov Chains
Continuous Probability Models
Statistics: Prologue
Fitting Continuous Models
The Family of Normal Distributions
Introduction to Statistical Inference
Multivariate Distributions
Dimension Reduction
Predictive Modeling
Model Parsimony and Overfitting
A. R Quick Start
B. Matrix Algebra

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.

https://www.routledge.com/Probability-and-Statistics-for-Data-Science-Math--R--Data/Matloff/p/book/9781138393295

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