A Hands-On Introduction to Data Science with Python
2nd Edition
Will be published approx. on 31. December 2025
Book
Hardback
400 pages
978-1-009-58892-8 (ISBN)
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 Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python 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.
More details
Edition
2nd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Edition type
Revised edition
Illustrations
Worked examples or Exercises
Dimensions
Height: 260 mm
Width: 208 mm
Thickness: 27 mm
Weight
1127 gr
ISBN-13
978-1-009-58892-8 (9781009588928)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
approx. 12/2025
2nd Edition
Cambridge University Press
€93.40
Not yet published
Person
Chirag Shah is Professor of Information and Computer Science at University of Washington (UW) in Seattle. He is the Founding Director for InfoSeeking Lab and Founding Co-Director of the Center for Responsibility in AI Systems & Experiences (RAISE). His research focuses on building, auditing, and correcting intelligent information access systems. Dr. Shah is a Distinguished Member of ACM as well as ASIS&T, and a Senior Member of IEEE. He has published nearly 200 peer-reviewed articles and authored several books, including textbooks on data science and machine learning. He regularly engages with industrial research labs at Amazon, ByteDance, Microsoft Research, and Spotify.
Content
Part I. Conceptual Introductions: 1. Introduction; 2. Data; Part II. Tools for Data Science: 3. Techniques; 4. Python; 5. Python 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.