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A Hands-On Introduction to Data Science

$49.99 (P)

textbook
  • Publication planned for: April 2020
  • availability: Not yet published - available from March 2020
  • format: Hardback
  • isbn: 9781108472449

$ 49.99 (P)
Hardback

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  • 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.

    • 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
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    Reviews & endorsements

    '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

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    Product details

    • Publication planned for: April 2020
    • format: Hardback
    • isbn: 9781108472449
    • dimensions: 253 x 195 x 25 mm
    • weight: 1.05kg
    • contains: 5 b/w illus. 135 colour illus. 36 tables 154 exercises
    • availability: Not yet published - available from March 2020
  • Table of Contents

    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.

  • Resources for

    A Hands-On Introduction to Data Science

    Chirag Shah

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  • 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.

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