A Hands-On Introduction to Data Science with R
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 2
- Publisher Cambridge University Press
- Date of Publication 22 January 2026
- ISBN 9781009589079
- Binding Hardback
- No. of pages406 pages
- Weight 1132 g
- Language English 697
Categories
Short description:
A hands-on textbook for introductory data science courses that use R.
MoreLong description:
Students 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.
MoreTable 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.
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