First, Noah Kauffman, an ex-MBA student of mine emailed me the story, then I found it in BusinessWeek, and a quick search brought up the story in many news sources, university websites, and magazines. Each had a different title. Here are some examples:MBA Students Are No. 1 – At Cheating (BusinessWeek, Oct 2 issue, page 14)A Crooked Path Through B-School (BusinessWeek Online)Study: Majority of Graduate Business Students Admit to Cheating (Penn State’s Smeal School of Business News and Media Resources)MBA Students Likelier to Cheat (Toronto Star)National survey: MBA cheating prevalent (The Cavalier Daily) All sources report the following about the … Continue reading Cheating in MBA programs

The ease of use of many data analysis and data mining software packages has lead to the dangerous tendency to jump to the model fitting stage without proper data exploration. Getting an initial understanding of the data via summarization and visualization is crucial for building good models. Mike Melcer, a current MBA student in my data mining class, mentioned that Bob Dylan knew this well. He sings You don’t need a weatherman to know which way the wind blows (from Subterranean Homesick Blues). The weatherman can, however, quantify the speed of the wind and the temperature. In other words, the … Continue reading Dylan on data exploration

The term “decision tree” has been used in two very different contexts, which causes some confusion. In the context of decision sciences (or decision making), it means a tree structure that assist in decision making, by mapping the different courses of action and assigning costs and probabilities to the different scenarios. There is a good description on MindTools website. In contrast, “decision trees” are also a popular name for classification trees (or regression trees), a data mining method for predicting an outcome from a set of predictor variables (see, for example, the description on Resample.com). Two well-known types of classification … Continue reading What are decision trees?