2nd Edition
Introduction to Data Science Statistics and Prediction Algorithms Through Case Studies
Part 1: Summary Statistics 1. Distributions 2. Nummercial Summaries 3. Comparing Groups Part 2: Probability 4. Connecting Data and Probability 5. Discrete Probability 6. Continuous Probability 7. Random Variables 8. Sampling Models and the Central Limit Theorem Part 3: Statistical Inference 9. Sampling Models and the Central Limit Theorem 10. Data-Driven Models 11. Bayesian Statistics 12. Hierarchical Models 13. Hypothesis Testing 14. Bootstrap Part 4: Linear Models 15. Introduction to Regression 16. The Linear Model Framework 17. Treatment Effect Models 18. Generalized Linear Models 19. Association Is Not Causation 20. Multivariable Regression Part 5: High Dimensional Data 21. Working with Matrices in R 22. Applied Linear Algebra 23. Dimension Reduction 24. Regularization 25. Latent Factor Models Part 6: Machine Learning 26. Notation and Terminology 27. Performance Metrics 28. Conditional Expectations and Smoothing 29. Resampling and Model Assessment 30. Supervised Learning Methods 31. Building Machine Learning Models 32. Unsupervised Learning: Clustering
Biography
Rafael A. Irizarry is Professor and Chair of the Department of Data Science at Dana-Farber Cancer Institute and Professor of Applied Statistics at Harvard. His research focuses on Genomics and he has taught several Data Science courses.
