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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- Sales Rank: #570190 in Books
- Published on: 2017-04-02
- Original language: English
- Dimensions: 9.20" h x .70" w x 7.30" l, .0 pounds
- Binding: Paperback
- 328 pages
About the Author
Brian Godsey, Ph.D., is a mathematician, entrepreneur, investor, and data scientist. He has worked in the analytic software industry, in academia, in finance, at the U.S. Department of Defense, and most recently as co-founder or early employee of data-centric start-ups, including Unoceros, Panopticon Labs, and RedOwl.
Most helpful customer reviews
3 of 4 people found the following review helpful.
Very basic...
By Al Krinker
I felt that the book lacked depth and it was just a collection of freely available material if one were to google on how to become data scientist. The book sort of organized the context for someone not to be all over the place and walked the reader starting out in the field of DS, but for someone who already has some experience in DS field this book would be too basic, so feel free to skip it.
Many examples that were given in the book (enron dataset, etc) are good examples and the ones that are generally used, but I wanted to see something new. So once again, I feel that this book is a collection of material that can be obtained freely off the web, all it did was to put it in one place for you to read. If you are just starting in the field of DS, then this book would save you time by having everything fundamental for you to read, however if you spent any time with DS already, much of the book would be something that you already saw before.
0 of 0 people found the following review helpful.
good description of data science process and concepts from beginning to end
By D.K.
This book really puts into perspective the stages of projects in data science, how they fit together, how you go from one to the next, and what are the important questions to ask at each phase. Insightful and thorough, beginning of a data science project through to the end.
One thing that this book seems to do that others don't is really get to the "why" of doing things in data science. It's doesn't just say "let's apply this machine learning program" but actually discusses the possibilities, with strengths and weaknesses, and essentially let's the reader decide what to do, with lots of guidance. It feels very deliberate and careful, which I thought was good.
Other reviewers are right, though, that it doesn't cover much advanced technical stuff, so if you're looking for that, this book isn't for you. I think that wasn't the point of this book, though. It's more about how to think about data and using it to solve problems and achieve goals through a process.
The book is also a little scattered at times; it's a little like stream-of-consciousness thoughts that could be organized better, but it's not that bad. It's actually kind of fun to read, at least compared to other software books. I do disagree with one reviewer's comment that this book doesn't contain much new information. I couldn't find most of the contents elsewhere, which is why I bought the book. Now I feel way more competent talking to my data science colleagues about what they're doing, and I'm probably a better manager, too, since I understand more about it now.
Overall, good book about process, goals, concepts, thought process, priorities, and not so much about how to do complex software development. Probably good for beginners, non-technical folks, as well as people who know how to write some code but don't really know where to start with data and data science (like me).
2 of 3 people found the following review helpful.
Good resource for beginners
By VG
A book for beginners in the topic. Having a title with 'Data Scientist' led me to believe that it might be an advanced book, but it is definitely for beginners and is not an intermediate title.
Leaving that aside, the book definitely covers a large number of topics, and use freely available datasets to help you to apply and learn them by hands-on practice. A newbie can definitely come out strong by going over the breadth of topics that this book covers.
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