I guess I’m proof positive that an old dog can learn new tricks. I was a bit concerned as I considered registering for the early-October statistical learning seminar in Boston. Though in possession of and familiar with the book The Elements of Statistical Learning by course instructors Trevor Hastie, Robert Tibshirani and colleague Jerome Friedman, it had been some time since I’d participated in an intensive, multiday training course. Besides, the trip would be quite expensive, and I wasn’t sure how receptive my partners would be to me skipping out for a few days and spending a large chunk of our discretionary funds. I ruminated on whether to commit when I first saw the announcement in midsummer, but the October 6-7 course dates closed the deal. I could spend all day Sunday the 5th gadding about Boston, visiting with friends, and maybe even experiencing live, post-season baseball.

On my early Sunday morning flight from Chicago to Boston, I prepped for Monday by reviewing the Breiman article discussed in my last column, as well as a thoughtful 1997 sister article by Jerome Friedman, entitled “Data Mining and Statistics: What’s the Connection?” Like Breiman, Friedman envisions an expanding academic analytics world - one that is progressing far beyond the purview of the traditional statistics currently taught in universities. He sees the then emerging field of data mining as a close match to statistics in the types of problems addressed, arguing that both statistics and data mining should make accommodations to more closely collaborate for the common good. Friedman cautions that an isolated discipline of statistics will lose students, researchers and standing to other, evolving information sciences.

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