Introduction to Probability and Statistics for Data Science
with R

For students in statistics, data science, engineering, and science programs needing a solid course in statistical theory and methods.

Steven E. Rigdon (Author), Ronald D. Fricker, Jr (Author), Douglas C. Montgomery (Author)

9781107113046, Cambridge University Press

Hardback, published 14 November 2024

828 pages
32.5 x 20.9 x 4.4 cm, 1.81 kg

'Excellent text book on probability and statistics. Highly recommended for computer science and data science students to understand data distribution and experimental validation.' Md Zia Ullah, Edinburgh Napier University

Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.

Part I. Descriptive Statistics & Data Science: 1. Introduction
2. Descriptive statistics
3. Data visualization
Part II. Probability: 4. Basic probability
5. Random variables
6. Discrete distributions
7. Continuous distribution
Part III. Classical Statistical Inference: 8. About data & data collection
9. Sampling distributions
10. Point estimation
11. Confidence intervals
12. Hypothesis testing
13. Hypothesis tests for two or more samples
14. Hypothesis tests for discrete data
15. Regression
Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods
17. Time series methods
18. The jackknife and bootstrap
Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees
20. Cross-validation and estimates of prediction error
21. Large-scale hypothesis testing and the false discovery rate
Appendix. More About R.

Subject Areas: Maths for computer scientists [UYAM]