I recently came across Ian Ayres' book: Super Crunchers. It is a nice read. Ayres essentially makes the case for number crunching (data mining for many of us) in all aspects of business and social life. The book describes a large number of case studies where number crunching has been successfully applied (e.g., wine quality, teaching methods, medical practices, etc.), often providing answers that challenge traditional wisdom. The examples are rather compelling. Most of the studies rely exclusively on random controlled trials and the use of regression techniques. Yet, I think this is a great book for people starting in data mining or looking for good reasons to begin. (The other nice thing is that the book is very cheap: less than $20 on Amazon!). Enjoy!
Wednesday, March 5, 2008
Ian Ayres' Super Crunchers Book
I recently came across Ian Ayres' book: Super Crunchers. It is a nice read. Ayres essentially makes the case for number crunching (data mining for many of us) in all aspects of business and social life. The book describes a large number of case studies where number crunching has been successfully applied (e.g., wine quality, teaching methods, medical practices, etc.), often providing answers that challenge traditional wisdom. The examples are rather compelling. Most of the studies rely exclusively on random controlled trials and the use of regression techniques. Yet, I think this is a great book for people starting in data mining or looking for good reasons to begin. (The other nice thing is that the book is very cheap: less than $20 on Amazon!). Enjoy!
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This book was previously reviewed here:
http://dataminingresearch.blogspot.com/2007/12/super-crunchers.html
http://dataminingresearch.blogspot.com/2008/02/small-book-review-super-crunchers.html
There is a very interesting rebuttal of the book by Bruce D. McCullough in SIGKDD Explorations (Vol. 10, No.1, June 2008). McCullough argues (in a rather compelling way) that 1) the book is not about number crunching (all of the case studies have relatively small datasets), and 2) Ayres is in no position to pass himself as a number cruncher. It certainly puts a damper on one's enthusiasm for the book. I recommend reading this short but well researched review by McCullough. Now, although I would agree, based on the evidence provided by McCullough, that there is less data mining in the book than there is traditional statistics, and that indeed Ayres may be pushing his luck, I think the book has at least some merit in raising awareness of the value of data analysis (vs. mere intuition) in business and society.
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