Introduction to Data Science

Two-Volume Set
2. Auflage 2026
ISBN: 978-1-032-51938-8
Verlag: Taylor & Francis

Buch, Englisch, 826 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 453 g

Reihe: Chapman & Hall/CRC Data Science Series

Unlike the first edition, the new edition has been split into two books, which have been brought together in this set.

Thoroughly revised and updated, the first book (Introduction to Data Science: Data Wrangling and Visualization with R) introduces skills that can help the reader tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. It includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises.

The second book (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.

Zielgruppe

Postgraduate, Undergraduate Advanced, and Undergraduate Core

Autoren/Hrsg.

Irizarry, Rafael A.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur

Inhaltsverzeichnis

Volume 1
Introduction Part 1: R 1. Getting started 2. R basics 3. Programming basics 4. The tidyverse 5. data.table 6. Importing data Part 2: Data Visualization 7. Visualizing data distributions 8. ggplot2 9. Data visualization principles 10. Data visualization in practice Part 3: Data Wrangling 11. Reshaping data 12. Joining tables 13. Parsing dates and times 14. Locales 15. Extracting data from the web 16. String processing 17. Text analysis Part 4: Productivity Tools 18. Organizing with Unix 19. Git and GitHub 20. Reproducible projects

Volume 2
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.