Description
Probability and Statistics for Data Science
Math + R + Data
Chapman & Hall/CRC Data Science Series
Author: Matloff Norman
Language: English
Subjects for Probability and Statistics for Data Science:
Keywords
Independent Geometric Random Variables; predictive learning; Cumulative Distribution Function; statistics for computer science; Negative Binomial Families; probability for computer science; BMI Data; predictive modeling; Negative Binomial; R; Multivariate Normal Family; statistical software; Committee Problem; statistics; Vice Versa; social networks; High BMI; probability; Preferential Attachment Model; neural networks; Dummy Variables; Monte Carlo simulation; Board Game; modeling; Random Variables; Markov chain; Mm Estimate; machine learning; Indicator Random Variable; hidden Markov model; Discrete Random Variables; duality; Multivariate Central Limit Theorem; derivatives; Bivariate Normal Density; data analysis; Log Linear Model; combinatorics; Continuous Random Variable; classification; Monty Hall Problem; calculus; Discrete Time Markov Chains; statistical inference; Cran Task View; UCI Machine Learn Repository; Transition Matrix
Publication date: 06-2019
· 15.2x22.9 cm · Hardback
Publication date: 06-2019
· 15.2x22.9 cm · Paperback
Description
/li>Contents
/li>Biography
/li>
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.
1. Basic Probability Models. 2. Discrete Random Variables. 3. Discrete Parametric Distribution Families. 4. Introduction to Discrete Markov Chains. 5. Continuous Probability Models. 6. The Family of Normal Distributions. 7. The Family of Exponential Distributions. 8. Random Vectors and Multivariate Distributions. 9. Statistics: Prologue. 10. Introduction to Confidence Intervals. 11. Introduction to Significance Tests. 12. General Statistical Estimation and Inference 13. Predictive Modeling
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.
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