Introduction to Data Science

Statistics and Prediction Algorithms Through Case Studies
2. Auflage 2026
ISBN: 978-1-032-41987-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 480 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Chapman & Hall/CRC Data Science Series

Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.

The book shows how to reason about variability, uncertainty, prediction error, model assumptions, and validation. Through case studies involving polling, genetics, baseball, recommendation systems, image classification, and other modern datasets, readers learn how to connect probability models to data, summarize complex information, quantify uncertainty, fit and interpret models, evaluate prediction algorithms, and understand the statistical ideas behind machine learning. Each chapter is designed to support classroom teaching, self-study, and hands-on analysis, with exercises and companion web materials available through the book website.

Key Features:

- Includes base R, data.table, and tidyverse code.

- Focuses on the statistical and probabilistic foundations of machine learning.

- Contains real-world case studies.

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.

Zielgruppe

Postgraduate, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Irizarry, Rafael A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

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

Über Autor(innen)

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.