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Keeping Up with the Quants: Your Guide to Understanding and Using Analytics Hardcover – June 11, 2013

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Thomas H. Davenport (Author)
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Thomas H. Davenport (Author), Jinho Kim (Author)
4 out of 5 stars 32 customer reviews
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  • Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
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Product Details

  • Hardcover: 240 pages
  • Publisher: Harvard Business Review Press (June 11, 2013)
  • Language: English
  • ISBN-10: 142218725X
  • ISBN-13: 978-1422187258
  • Product Dimensions: 0.8 x 6.2 x 9.5 inches
  • Shipping Weight: 15.5 ounces (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (32 customer reviews)
  • Amazon Best Sellers Rank: #121,518 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starsFor Playing Well with Quants and Their Data
By Mark Graham on June 13, 2013
Format: Hardcover
With data analysis becoming more important in every kind of business and organization (highlighted by Netflix, Amazon, Moneyball, and Nate Silver's political blog 538 among many others), this book offers timely advice. Keeping Up with the Quants explains the basic groundwork of the types and workings of data analysis. However, you need not be a quant to understand this book. In fact it is geared to non-quants who want to better understand how to work with the number crunchers/analysts. Even if you don't actively do the statistics yourself, it is still helpful to get up to speed on how to think about the bigger picture for using analytics.

Other books likely to be helpful include Nate Silver's bestseller The Signal and the Noise: Why So Many Predictions Fail -- but Some Don'twhich makes statistical thinking fun to read about. A Lessor known work (which I learned about it in Silver's book) is Paul E. Meehl's classic Clinical Versus Statistical Prediction. This book is definitely more technical than Silver's but is geared to working with softer, psychological/behavioral issues (for reviews of the out of print edition go here: Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, for a lessor expensive edition here: Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence(at this writing only the out of print edition has reviews, but it can be pricey).
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3.0 out of 5 starsInteresting but perhaps oversimplified
By Lauren Hirs on August 11, 2013
Format: Hardcover
Keeping Up With the Quants was interesting enough to keep me (a 9-year veteran of a predictive analytics field) reading to the end, but I frequently felt like the book was oversimplified - even for "non-quants." If there are professionals out there who don't know some of these basics, I'm scared for the future of business. If you already know different chart types, statistical basis like R-squared and regressions, and that correlation does not equal causation, you may want to skip. That said, it's a quick enough read that it's worth a look for the gems hidden within it.

I'd judge this an ideal book for a student or new grad looking to understand basic concepts of analytics, but maybe not for experienced workers.
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5.0 out of 5 starsWhat non-quants need to understand in order to take full advantage of the results that quantitative analysis can produce
By Robert Morris HALL OF FAMETOP 500 REVIEWERVINE VOICE on June 18, 2013
Format: Hardcover
Almost everything most business leaders know about analytics they have learned from Tom Davenport and the material he provides in his books, notably in Competing in Analytics and Analytics at Work. In a more recent book, Judgment Calls, he and co-author Brooke Manville offer "an antidote for the Great Man theory of decision making and organizational performance": organizational judgment . That is, "the collective capacity to make good calls and wise moves when the need for them exceeds the scope of any single leader's direct control." The more non-quants there are in a given organization, the better prepared that organization will be to take full advantage of the results that quantitative analysis can produce, hence the importance of Judgment Calls.

Davenport wrote Keeping Up with the Quants in collaboration with Jinho Kim to demonstrate how quantitative analysis works -- even if (especially if) their reader does not have a quantitative analysis background -- and how to make better decisions. "Analytics can be classified as descriptive, predictive, or prescriptive according to the given methods, [applications,] and purpose." They examine three analytical thinking stages and how to apply them and then shift their attention to six steps of quantitative analysis. I commend them on their skillful use of various reader-friendly devices that include boxed mini-commentaries (e.g. "The Danger of NOT Thinking Analytically," Pages 19-21), Worksheets, Tables (e.g. 5-1, "Data-mining software for finding patterns in data," Page 140), Figures (e.g. 6.1, "The process of becoming a proficient quantitative analyst," Page 156), a "Summary" at the conclusion of Chapters 1-13, and real-world examples of what can be learned (i.e.Read more ›
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5.0 out of 5 starsAwesome Text for Aspiring Data Scientists AND Users...
By Let's Compare Options Preptorial TOP 500 REVIEWER on June 10, 2013
Format: Hardcover
If you read the literature about new grads unable to find jobs except at McD's, one exception is a new category of job and profession called "Data Scientist" which started being used as late as 2009/2010. There now even is a CDSO--Chief Data Science Officer-- in some data intensive firms! IF your team doesn't yet have a CDSO, tell them to create the position and give it to you, especially if you're reading this review and considering purchasing this book. Entry data science: $90K range; Manager: $165K; CDSO: $220K including options, etc. (Source: DS assn.).

This little gem won't turn you into a data wonk overnight, but it IS a "condensed" Data/ MBA course (especially quantitative analysis in IT) showing the most current aspects of analysis from a big data vantage point. To be brutally honest, folks who got their MBAs even as recently as four years ago, and studied linear programming etc., are now having to once again reinvent themselves with the much more heavily statistical nature of mining, machine learning, Web X.o, etc.

My premise is: to be successful, we ALL need to be data scientists today, whether or not it fits our current job description! If you see any truth in that at all, this book is a must read. It avoids the deep math and algorithms behind the analysis, but gives very clear analogies, examples, and techniques in English to help with the most important steps: interpretation, relevance and use.

A recent trend in big data is "ala carte" dashboards, where users configure Sharepoint and other tools via Excel etc. LOCALLY. If you're not a quant, you ARE a quant CUSTOMER, and deserve to maximize the tools you have in front of you. Asking the right questions is 75% of getting that benefit.Read more ›
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Most Recent Customer Reviews

5.0 out of 5 starsFive Stars

Excellent book, good to read an interesting examples! Highly recommended

Published 28 days ago by Amazon Customer
5.0 out of 5 starsFive Stars

A must read for anyone in business today

Published 1 month ago by Amazon Customer
5.0 out of 5 starsFive Stars

Great Book!

Published 5 months ago by Amazon Customer
5.0 out of 5 starsExcellent

"How to develop habits of quantitative thinking"... is one of the main concepts that I have learned from this book. Excellent recommendation

Published 5 months ago by Rodrigo Baccaro
5.0 out of 5 starsFive Stars

great book

Published 5 months ago by Amazon Customer
3.0 out of 5 starsGood book for your reference library

This is a good book of check lists. They cover a lot of good reminders about how we mere mortals can work with quantitative analysis.

Published 7 months ago by Gregory Dellacroce
5.0 out of 5 starsExcellent

Excelleny

Published 7 months ago by Mark Chrzanowski
5.0 out of 5 starsGreat Quants

The book is just like new. I did not think I would like the content, but it's pretty interesting.

Published 8 months ago by qwerty
5.0 out of 5 starsThis book is an Awesome all round book for beginners

I am not a beginner but not a practitioner either. I have undergone a corporate training program in Data Science by NCSU. My review is for the audio version. Read more

Published 11 months ago by Devarajulu L.
5.0 out of 5 starsUnderstanding/Learning to Utilize New Data/Analytic Capabilities

This book offers a useful means for keeping up with the burgeoning field of analytics. As Davenport and Kim state “the world is awash with data” and their aim is to explain how... Read more

Published 14 months ago by Fred Cheyunski

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