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Think Like a Data Scientist: Tackle the data science process step-by-step
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Product details
Paperback: 328 pages
Publisher: Manning Publications; 1 edition (April 2, 2017)
Language: English
ISBN-10: 9781633430273
ISBN-13: 978-1633430273
ASIN: 1633430278
Product Dimensions:
7 x 0.8 x 9 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
6 customer reviews
Amazon Best Sellers Rank:
#553,423 in Books (See Top 100 in Books)
This book describes exactly what it’s like to look at things from a data scientist perspective.
Reviewers who dismiss this book as too elementary should have read the excerpts in the listing: the author addresses this situation. There are parts that are already familiar to me, but considering them as parts of a well-defined process puts them in a new perspective.To the reviewer who dismisses it by saying that all of the information is available on the web, I say "Yes, and I've collected tons of it; the problem is similar to the problea facing a data scientist: diverse data sets that ovelap -- but in ways that make it extraordinarily difficult and time consuming to align them usefully." Having it all presented in the context of a logical, coherent process is like having a real meal, not just scraping together whatever leftovers happen to be in the fridge today.I shopped around a lot before settling on Godsey's book, and at the halfway point I'm still thoroughly convinced that I chose wisely.The principal difference between TLADS and every other book I evaluated is that Godsey's emphasis is on PROCESS rather than tools and methods. He addresses the latter, but this is not Yet Another Book About How To Do Data Science With { R | Python }: there are plenty of those out there, and I've picked the ones I uant to use -- but AFTER I've learned about the art and craft of the discipline of data science. To me, it makes little sense to learn how to use woodworking tools before learning about how to make furniture (or frame a house, or...). That's one of Godsey's analogies, BTW.Godsey is a very good writer -- not always true of technical authors -- and an excellent teacher. He knows how to express the technical content in a manner that's approachable but not condescending: Data Science For Dummies this is emphatically NOT. And because I've been working for 30 years in an area of AI that requires some of the same skills as data science, I know from personal experience that the techniques and processes Godsey elaborates on are dead-on accurate, and just as critical to the data gathering and "munging" process as he says they are.If you're looking for a book on doing data science from a hands-on, technical POV, you can choose from the many books that focus on this.If you want to understand how to pursue a career in data science in the real world -- how to BE a data scientist -- look no further.
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.I like the writing style. It's a little like stream-of-consciousness thoughts maybe could be organized better, but it really gives the feeling that you know what a data scientist should be thinking. 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).
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.
This is a great intro text to the field. The examples are useful, and the informal writing style makes the subject accessible to anyone with a basic math or engineering background.
It gives a very broad overview instead of deep dive on technologies, I found it's very boring to read this book.
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