A Hands-On Introduction to Data Science

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Chirag Shah (Author)

9781108472449, Cambridge University Press

Hardback, published 2 April 2020

424 pages, 5 b/w illus. 135 colour illus. 36 tables 154 exercises
25.3 x 19.5 x 2.5 cm, 1.16 kg

'Dr. Shah has written a fabulous introduction to data science for a broad audience. His book offers many learning opportunities, including explanations of core principles, thought-provoking conceptual questions, and hands-on examples and exercises. It will help readers gain proficiency in this important area and quickly start deriving insights from data.' Ryen W. White, Microsoft Research AI

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Part I. Introduction: 1. Introduction
2. Data
3. Techniques
Part II. Tools: 4. UNIX
5. Python
6. R
7. MySQL
Part III. Machine Learning: 8. Machine learning introduction and regression
9. Supervised learning
10. Unsupervised learning
Part IV. Applications and Evaluations: 11. Hands-on with solving data problems
12. Data collection, experimentation and evaluation.

Subject Areas: Machine learning [UYQM], Data mining [UNF], Databases [UN], Computing & information technology [U], Knowledge management [KJMV3], Business & management [KJ]