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Steve Miller, Information Management Blogs, November 7, 2011
In the clutter I found two copies of
The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani and Jerome Friedman - one I purchased two years ago and the other I received at a recent
Statistical Learning and Data Mining (SLDM III) seminar taught by first two authors. ESL is quite popular in the predictive modeling world, often referred to by aficionados as "the book", "the SL book" or the "big yellow book" in reverence to its status as the SL bible.
Hastie, Tibshirani and Friedman are Professors of Statistics at Stanford University, the top-rated stats department in the country. For over 20 years, the three have been leaders in the field of statistical learning and prediction that sits between traditional statistical modeling and data mining algorithms from computer science. I was introduced to their work when I took the SLDM course three years ago.
...
I asked
Trevor Hastie
in Boston if he'd be willing to do an interview for Information Management and he graciously agreed. Below are his thoughtful answers to my questions.
1) Where does statistical learning fit with traditional statistics and data mining?
For me "statistical learning" is a more evocative phrase than "applied statistical modeling", which is a term from statistics, but they essentially mean the same, with the former perhaps emphasizing prediction. The phrase data mining comes from computer science and engineering, where we imagine large troves of data (typically gathered in some automatic fashion), and we are searching for structure, and perhaps predictive targets.
Many ingenious techniques have been invented in this domain (e.g. boosting, neural networks), often with an algorithmic flavor. Statistical learning for me tries to understand these new techniques in terms of statistical models, and connect them with other more traditional approaches.
...
5) Do you agree with Hal Varian that the "sexy job in the next ten years will be statistician ... The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades."?
Isn't that great. When I was at school, we were the nerds. Now we are sexy! Wish I could be back at school. Clearly data analysis and modeling has become fundamental in many areas of science, technology and business. As our abilities to gather and store data improve, so does the need increase to make sense of it.
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