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Turning Numbers into Knowledge Kindle Edition
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About the Author
- File Size : 26744 KB
- Word Wise : Enabled
- Language: : English
- ASIN : B076DM4691
- Publication Date : November 1, 2017
- Publisher : Analytics Press; 3rd Edition (November 1, 2017)
- Lending : Enabled
- Print Length : 251 pages
- X-Ray : Not Enabled
- Enhanced Typesetting : Enabled
- Text-to-Speech : Enabled
- Best Sellers Rank: #461,794 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
Top reviews from the United States
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Since receiving it, I have read it twice: once cover-to-cover to see how the author put everything together and, more recently, as a reference while working on data analysis projects where procedures and best practices come into question. I liked the explanations contained within the book, and the anecdotes to help exemplify the lessons -- it is a great "After Action Report" from others working in the realm of data analysis.
My background, both education and experience, is primarily in economic analysis. But, since the explosion of data science, I would say that what I've done for the past 10 years is in the gray area that is data analysis: I've worked with economic data ranging from income tax returns to employment to sales/financial data to logged internet usage data.
This is *not* a mathematically-detailed book, full of code, that will make you an instant data scientist; the author even warns of that in the book's Preface. Instead, I took the book for what it is: a collection of lessons learned that can be important to those working in a data analytical role as well as for those who work with those in a data analytics role (the analysts' "customers", manager(s), etc.). The audience for this book, therefore, is not just the "hard core" data scientist or machine learning programmer, but for anyone working within the data analytics space.
I'd like to thank Dr. Koomey for giving me the opportunity to read his book. It's on my shelf with several Post-It Notes acting as reference to the sections that I found most informative/interesting alongside my R and python reference books and economics texts.
My favorite section is Chapter 34, the authors give an overview of how to present data in a way that is easily readable and accurate, while eliminating “noise” and superfluous data. It’s amazing how a few formatting tweaks and highlights can drive home the real message, which is often lost in bullet points, over-fanciful charts, or rows of extraneous data. I especially like the side-by-side comparisons of different data representations, which as the authors say, “let the data do the work”, which has helped me countless times over the years as an analyst and now as a science teacher. This is definitely a book I will use in my classroom and I have already recommended it to two friends who “crunch numbers” regularly (they are tired of hearing my diatribes about graphs and now I have some tangible backup!) .
Whether you work with data or are just looking to understand the world better, this book is a great resource!