A Hands-On Introduction to Data Science
Published on 2. April 2020
Book
Hardback
424 pages
978-1-108-47244-9 (ISBN)
Description
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.
Reviews / Votes
'Chirag's extensive experience as a teacher shines through in this textbook, which lives up to its promise to be a 'hands on' introduction to data science. Students have a chance to apply their learning to real-life examples from diverse fields, with hands-on examples that build on basic techniques and utilize tools of data science practice throughout the book. I am particularly pleased to see him weave human issues into his approach, putting principles ahead of particular tools and pointing to ethical challenges at various stages of working with data to help his audience develop an appreciation of ways context and interpretation shape data practices. He exposes students to a more nuanced perspective in which human as well as machine input shapes data science outcomes. It is an awareness that we all will need if we are to use data appropriately to tackle the complex challenges we face today.' Theresa Dirndorfer Anderson '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 AIMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises; 36 Tables, black and white; 135 Halftones, color; 5 Line drawings, black and white
Dimensions
Height: 260 mm
Width: 208 mm
Thickness: 29 mm
Weight
1197 gr
ISBN-13
978-1-108-47244-9 (9781108472449)
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
Chirag Shah
A Hands-On Introduction to Data Science
E-Book
01/2020
Cambridge University Press
€41.99
Available for download
Person
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.
Content
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.