Probability and statistics for data science : Norman Matloff.

By: Matloff, Norman SMaterial type: TextTextSeries: Series in computer science and data analysisPublication details: Boca Raton : CRC Press, Taylor & Francis Group, 2020Description: xxxii, 412 pages ; illustrations ; 24 cmISBN: 9780367260934; 9781138393295; 1138393290Subject(s): Probabilities | Mathematical statisticsDDC classification: 519.5
Contents:
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
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.
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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

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.

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