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

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

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