A Hands-On Introduction to Data Science with R
A Hands-On Introduction to Data Science with R
£44.99
GBPStudents will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
- Develop a practical understanding of data science by working through hands-on problems, exercises and examples using the popular R platform
- Go from absolute beginner to working data scientist with 11 accessible chapters that assume no prior technical background
- See how concepts are applied within an industry context with all new 'Data Science in Practice' boxes
- Teach data science with end-to-end support, including curriculum suggestions, sample syllabi, lecture slides, datasets, additional assessment material and a solutions manual, available for registered instructors
Product details
October 2025Hardback
9781009589079
400 pages
254 × 203 mm
Not yet published - available from October 2025
Table of Contents
- Part I. Conceptual Introductions:
- 1. Introduction
- 2. Data
- Part II. Tools for Data Science:
- 3. Techniques
- 4. Introduction to R
- 5. R for Statistical Analysis
- 6. Cloud Computing
- Part III. Machine Learning for Data Science:
- 7. Machine Learning Introduction and Regression
- 8. Supervised Learning
- 9. Unsupervised Learning
- Part IV. Applications, Evaluations, and Methods:
- 10. Data Collection, Experimentation, and Evaluation
- 11. Hands-On with Solving Data Problems.
