Norman Matloff

Probability and Statistics for Data Science

Math + R + Data

• Gebundenes Buch

Produktbeschreibung

This text is designed for a one-semester junior/senior/graduate-level calculus-based course on probability and statistics, aimed specifically at data science students (including computer science). In addition to calculus, the text assumes basic knowledge of matrix algebra and rudimentary computer programming.

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Produktdetails

• Verlag: Taylor and Francis
• Seitenzahl: 412
• Erscheinungstermin: 25. Juni 2019
• Englisch
• Abmessung: 236mm x 157mm x 28mm
• Gewicht: 771g
• ISBN-13: 9780367260934
• ISBN-10: 036726093X
• Artikelnr.: 57045644

Autorenporträt

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.

Inhaltsangabe

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