A Hands-On Introduction to Data Science with R
Cambridge University Press (Verlag)
978-1-009-58907-9 (ISBN)
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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.
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.
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.
| Erscheint lt. Verlag | 31.12.2025 |
|---|---|
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Gewicht | 500 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-10 | 1-009-58907-5 / 1009589075 |
| ISBN-13 | 978-1-009-58907-9 / 9781009589079 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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