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Book details
Paperback: 328 pages
Format: epub
Size: 1.36 MB
Publisher: Manning Publications; 1 edition (April 2, 2017)
Language: English
ISBN-10: 1633430278
ISBN-13: 978-1633430273

Tackle the Data Science Process Step-by-step
About the Reader

Readers need beginner programming skills and knowledge of basic statistics.

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 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
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
PART 2 – BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS
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
PART 3 – FINISHING OFF THE PRODUCT AND WRAPPING UP
Delivering a product
After product delivery: problems and revisions
Wrapping up: putting the project away