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Thinking with Data: How to Turn Information into Insights Paperback

ISBN-13: 978-1449362935 ISBN-10: 1449362931 Edition: 1st

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Thinking with Data: How to Turn Information into Insights + Doing Data Science: Straight Talk from the Frontline + Data Science for Business: What you need to know about data mining and data-analytic thinking
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Product Details

  • Paperback: 94 pages
  • Publisher: O'Reilly Media; 1 edition (February 3, 2014)
  • Language: English
  • ISBN-10: 1449362931
  • ISBN-13: 978-1449362935
  • Product Dimensions: 9 x 6.4 x 0.2 inches
  • Shipping Weight: 4.8 ounces (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (6 customer reviews)
  • Amazon Best Sellers Rank: #138,509 in Books (See Top 100 in Books)

Editorial Reviews

Book Description

How to Turn Information into Insights

About the Author

Max Shron runs a small data strategy consultancy in New York, working with many organizations to help them get the most out of their data. His analyses of transit, public health, and housing markets has been featured in The New York Times, Chicago Tribune, Huffington Post, WNYC, and more. Prior to becoming a data strategy consultant, he was the data scientist for OkCupid.


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Customer Reviews

4.2 out of 5 stars
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Most Helpful Customer Reviews

6 of 6 people found the following review helpful By Alan on January 29, 2014
Format: Kindle Edition Verified Purchase
This book is no longer than it needs to be and the title for 1 of it's 6 chapters is 'Causality' - how could I NOT like this book!

I highly recommend this book to anyone who is involved in the development of software products. This is because above all else, it's book about critical thinking within the context of product - and even more specifically, how to use Data to improve our products.

This book sits in a sweet spot of being high level enough to keep the content flowing as well as peppering it with pin point examples that succinctly illustrate the author's point. The author doesn't waste words overemphasizing points or tying concpets to any specific engineering or project management discipline. This should be appreciated as it respects both the reader's intelligence and time.

If your a product manager, engineer, designer...or anyone else involved in creating and growing products, I recommend this book to you.

Here is an excerpt which conveys my point. This is from Chapter 1 - Scoping: Why Before How:

"...Rather than saying, "The manager wants to know where users drop out on the way to buying something," consider saying, "The manager wants more users to finish their purchases. How do we encourage that?" Answering the first question is a component of doing the second, but the action-oriented formulation opens up more possibilities, such as testing new designs and performing user experience interviews to gather more data.

If it is not helpful to phrase something in terms of an action, it should at least be related to some larger strategic question.
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8 of 9 people found the following review helpful By Charles G. on February 4, 2014
Format: Paperback Verified Purchase
I'm not really sure what I was expecting when I bought this book, but I can definitely say that the book disappointed me. First of all, it is really short. Secondly, I would say that a lot of the content seems like a mixture of project management and basic critical thinking/analytic skills as opposed to anything specific to data science. Skip it and buy Data Science for Business.
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4 of 4 people found the following review helpful By T. Yokota on January 29, 2014
Format: Kindle Edition Verified Purchase
"We want to know Y given X. Here's the data. I want a report by Z."

Sounds familiar? If yes, then this book is a must read, regardless of where you sit on the table (analyst or requester). This book is about the importance of collaboration and planning for a data analysis project. Sure. Many businesses will swear that such a process exists, but how well is it working? This book provides useful guidelines and suggestions that can help in evaluating your business's data culture so hat meaningful outcomes are derived that will lead to purposeful action.
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