Think Like a Data Scientist: Tackle the data science process step-by-step

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

PART 1 – PREPARING AND GATHERING DATA AND KNOWLEDGE
Chapter 1. Philosophies Of Data Science
Chapter 2. Setting Goals By Asking Good Questions
Chapter 3. Data All Around Us: The Virtual Wilderness
Chapter 4. Data Wrangling: From Capture To Domestication
Chapter 5. Data Assessment: Poking And Prodding

PART 2 – BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS
Chapter 1. Developing A Plan
Chapter 2. Statistics And Modeling: Concepts And Foundations
Chapter 3. Software: Statistics In Action
Chapter 4. Supplementary Software: Bigger, Faster, More Efficient
Chapter 5. Plan Execution: Putting It All Together

PART 3 – FINISHING OFF THE PRODUCT AND WRAPPING UP
Chapter 1. Delivering A Product
Chapter 2. After Product Delivery: Problems And Revisions
Chapter 3. Wrapping Up: Putting The Project Away

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