Internet Explorer 11 is being discontinued by Microsoft in August 2021.
If you have difficulties viewing the site on Internet Explorer 11 we
recommend using a different browser such as Microsoft Edge, Google
Chrome, Apple Safari or Mozilla Firefox.
Last updated 26/06/24: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website https://www.cambridge.org/news-and-insights/technical-incident
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…
Almost everything in the book is accompanied with examples and practice - both in-chapter and end-of-chapter so students are more engaged because they can use hands-on experiences to see how theories relate to solving practical problems
Assumes no prior technical background or computing knowledge and lowers the barrier for entering the field of data science so that students from a range of disciplines can benefit from a more accessible introduction to data science
Supplemented by a generous set of material for instructors, including curriculum suggestions and syllabi, slides for each chapter, datasets, program scripts, answers and solutions to each exercise, as well as sample exams and projects which gives instructors end-to-end support for teaching a data science course
If you believe you should have access to this content, please contact
your institutional librarian or consult our
FAQ page
for further information about accessing our content.
Also available to purchase from these educational ebook suppliers
Author
Chirag Shah,University of Washington
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.