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.

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.

'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

Autorentext
Chirag Shah is an Associate Professor of Information and Computer Science at Rutgers University, New Jersey. He investigates issues of search and recommendations using data mining and machine learning. Dr Shah received his M.S. in Computer Science from the University of Massachusetts, Amherst, and his Ph.D. in Information Science from the University of North Carolina, Chapel Hill. He directs the InfoSeeking Lab, supported by awards from the National Science Foundation, the National Institute of Health, the Institute of Museum and Library Services, as well as Amazon, Google, and Yahoo. He was a Visiting Research Scientist at Spotify and has served as a consultant to the United Nations Data Analytics on various data science projects. He is currently working on large-scale e-commerce data and machine learning problems as Amazon Scholar.

Zusammenfassung
A practical introduction to data science with a low barrier entry, this textbook is well-suited to students from a range of disciplines. Assuming no prior knowledge of the subject, the hands-on exercises and real-life application of popular data science tools are accessible even to students without a strong technical background.

Inhalt

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.