Think Like a Data Scientist: Tackle the data science process step-by-step
內容描述
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
PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS
PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP
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
