- Taschenbuch: 91 Seiten
- Verlag: O'Reilly and Associates; Auflage: 1 (31. Januar 2014)
- Sprache: Englisch
- ISBN-10: 1449362931
- ISBN-13: 978-1449362935
- Größe und/oder Gewicht: 15 x 0,6 x 22,4 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 83.898 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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Thinking with Data: How to Turn Information into Insights (Englisch) Taschenbuch – 31. Januar 2014
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Über den Autor und weitere Mitwirkende
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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Die hilfreichsten Kundenrezensionen auf Amazon.com
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. For example, understanding how users of a product are migrating from desktop to mobile versions of a website is useful for informing the product strategy, even if there is no obvious action to take afterward..."
Also, Amazon doesn't have a table of contents for this book so here it is:
1. Scoping: Why Before How
2. What's Next?
4. Patterns of Reasoning
6. Putting It All Together
A. Further Reading
The book has very few pages, but provides lots of useful information and serves as, as the book’s last sentence indicates, “...a clear place to start for every beginner.”
I learned a lot from the book and will be going through it a second time. I did not take notes during this first pass but will do so during the next.
While I found this book to be quite good (to the point and perfect for my needs), I completely agree w/ what 2-star reviews are saying: This is not for people who already have a background in solving problems or advancing products w/ data.
This book is for anyone jumping into data science or a role that requires critical thinking or use of data to solve a problem. But if you already know how to structure problems, how to identify needed pieces of data to reach a solution, or have already successfully managed data-related projects, this book may be a little too elementary for you.
The book touches on basics of scoping a project, making arguments, reasoning, and causality. In the final chapter it applies all that it has taught on 2 realistic use cases and summarizes the process in the last page.
Again, if new to the field, read it. If already seasoned, skip it.