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Book Description

Summary

Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems.

About the Technology

Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there.

About the Book

Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you’ll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you’ll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you’ll put this knowledge together using a structured process for data science. When you’ve finished, you’ll have a strong foundation for a lifetime of data science learning and practice.

What’s Inside

  • The data science process, step-by-step

  • How to anticipate problems

  • Dealing with uncertainty

  • Best practices in software and scientific thinking

  • About the Reader

    Readers need beginner programming skills and knowledge of basic statistics.

    About the Author

    Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups.

    Table of Contents

    1. Copyright
    2. Brief Table of Contents
    3. Table of Contents
    4. Preface
    5. Acknowledgments
    6. About this Book
    7. About the Cover Illustration
    8. Part 1. Preparing and gathering data and knowledge
      1. Chapter 1. Philosophies of data science
      2. Chapter 2. Setting goals by asking good questions
      3. Chapter 3. Data all around us: the virtual wilderness
      4. Chapter 4. Data wrangling: from capture to domestication
      5. Chapter 5. Data assessment: poking and prodding
    9. Part 2. Building a product with software and statistics
      1. Chapter 6. Developing a plan
      2. Chapter 7. Statistics and modeling: concepts and foundations
      3. Chapter 8. Software: statistics in action
      4. Chapter 9. Supplementary software: bigger, faster, more efficient
      5. Chapter 10. Plan execution: putting it all together
    10. Part 3. Finishing off the product and wrapping up
      1. Chapter 11. Delivering a product
      2. Chapter 12. After product delivery: problems and revisions
      3. Chapter 13. Wrapping up: putting the project away
    11. Appendix. Exercises: Examples and Answers
    12. The lifecycle of a data science project
    13. Index
    14. List of Figures
    15. List of Tables
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