Think Like a Data Scientist: Tackle the Data Science Process Step-by-Step
- 7h 38m
- Brian Godsey
- Manning Publications
- 2017
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.
In this Book
-
About This Book
-
Philosophies of Data Science
-
Setting Goals by Asking Good Questions
-
Data all around Us—The Virtual Wilderness
-
Data Wrangling—From Capture to Domestication
-
Data Assessment—Poking and Prodding
-
Developing a Plan
-
Statistics and Modeling—Concepts and Foundations
-
Software—Statistics in Action
-
Supplementary Software—Bigger, Faster, More Efficient
-
Plan Execution—Putting it all Together
-
Delivering a Product
-
After Product Delivery—Problems and Revisions
-
Wrapping Up—Putting the Project Away
-
Exercises—Examples and Answers
-
The Lifecycle of a Data Science Project
