Tomorrow at 11:00 EST I will be giving a webcast describing several term projects by MBAs in my data mining class. Students have been working on real business projects in my class for 4 years now, with many of the projects leading to important insights to the companies who provided the data (in most cases the students’ workplaces). For each of several cases I will describe the business objective; we’ll look at the data via interactive visualization using Spotfire, and then examine some of the analyses and findings. The webcast is organized by Spotfire (now a division of TIBCO). We … Continue reading Webcast on Analytics in the Classroom
Some get a chill when they hear “data mining” because they associate it with “big brother”. Well, here’s one more major incident that sheds darkness on smart algorithms: The Department of Homeland Security declared the end of a data mining program called ADVISE (Analysis, Dissemination, Visualization, Insight and Semantic Enhancement). Why? Because it turns out that they were testing it for two years on live data on real people “without meeting privacy requirements” (Yahoo! News: DHS ends criticized data-mining program). There is nothing wrong or evil about data mining. It’s like any other tool: you can use it or abuse … Continue reading Data mining = Evil?
A new book is gaining emotional reactions for the normally calm statistics community (no pun intended): The Black Swan: The Impact of the Highly Improbably by Nassim Taleb uses blunt language to critique the field of statistics, statisticians, and users of statistics. I have not yet read the book, but from the many reviews and coverage I am running to get a copy. The widely read ASA statistics journal The American Statistician decided to devote a special section that reviews the book and even obtained a (somewhat bland) response from the author. Four reputable statisticians (Robert Lund, Peter Westfall, Joseph … Continue reading Shaking up the statistics community