PDF Download Turning Numbers into Knowledge: Mastering the Art of Problem Solving
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Turning Numbers into Knowledge: Mastering the Art of Problem Solving
PDF Download Turning Numbers into Knowledge: Mastering the Art of Problem Solving
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Review
"A lively, well-written, attractively packaged book on the art of critical thinking." —Skeptical Inquirer“This outstanding book teaches the tricks of the analytical trade. There’s no better guide to learning how to use numbers to understand the world.”—Art Rosenfeld, Professor Emeritus of Physics, University of California at Berkeley "Dr. Koomey's book deserves to be widely read and shared, especially by those who take seriously the fragile yet critical role of an informed citizenry in increasingly complex democratic societies." —Professor Michael Maniates, Yale-NUS College "The greatest challenge facing educators, institutions, and businesses is the inculcation of rigor and fact-based analysis into the psyches of future leaders. Turning Numbers into Knowledge is the tool for organizations to accomplish this essential task." —Ephraim Heller, founder, Therasense, Inc., Angioscore Inc, and SynAgile Inc."This book is a great tool for improving your own use of numbers and for seeing through the smoke screens of others." —Lee Schipper, PhD, formerly senior scientist, International Energy Agency"This book is a terrific resource for students, and an essential aid for researchers, from undergraduates on up. Required reading!" —Eban Goodstein, professor, department of economics, Lewis and Clark College"There is nothing else like this book out there. Nobody who deals with problems where numbers matter—and everybody in today’s world really needs to—should be without it." —John P. Holdren, past president, American Association for the Advancement of Science
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About the Author
Dr. Jonathan Koomey is a lecturer at Stanford University. His academic work, summarized in nine books and more than 200 articles, spans engineering, economics, public policy, and environmental science. Dr. John P. Holdren was Director of the White House Office of Science and Technology Policy and Co-Chair of the President's Council of Advisors on Science and Technology in the Obama administration. Before that he was a professor and program director at Harvard University and Woods Hole Research Center. His awards include a MacArthur Foundation Prize Fellowship and the John Heinz Prize in Public Policy, among others. He returned to Harvard as a professor in 2017.
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Product details
Paperback: 251 pages
Publisher: Analytics Press; 3 edition (October 1, 2017)
Language: English
ISBN-10: 1938377060
ISBN-13: 978-1938377068
Product Dimensions:
7 x 0.8 x 10 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#725,327 in Books (See Top 100 in Books)
I read as part of the DIY MBA not expecting much and instead, very much enjoyed it.
I was lucky to have one of the authors as a visiting lecturer while pursuing my masters degree in engineering, and have applied these lessons in my personal and professional life. The book is built on the idea that improving problem solving skills at a basic level can lead to much greater understanding--and subsequently presenting information in a meaningful manner.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 workâ€, 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!
First, I received this book in December prior to its publication to review via Data Science Central.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.
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