More and more data about people and organizations are being collected and present-day computing power also allows such data to be analyzed and used for analytic and predictive purposes. Although in many cases actual decision-making is based on data, there are still surprisingly many instances where using relatively straightforward models works just as good or better than having a human expert decide, but are nevertheless not implemented.
The fields of application vary widely. That the price of a bottle of Bordeaux can be adequately predicted on the basis of just four straightforward indicators (outperforming expensive and experienced experts), is in itself a rather innocent finding. But similar cases can be made, sometimes, for life-and-death decisions: there is evidence that whether you are having a heart attack or not can be diagnosed quite accurately using simple models, just as well as experienced physicians can do this.
The course gives students an in-depth understanding of the literature on model-based versus human decision making, based on popular accounts of data science efforts (see below) and their academic background, with an emphasis on the way in which humans and models deal with large amounts of information.
Though the principles we consider largely hold across domains, and we offer examples from a broad area of applications, we emphasize examples from marketing, health, and behavioral operations management
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program.
Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS).
Students learn hands-on what the typical pitfalls are of dealing with (large scale) information, and of explaining the benefits of model-based predictions to a general audience. This includes both the knowledge of the general process characteristics of adequate data scientific efforts, a general sense of the ideas and prejudices of humans involved in or confronted with such efforts, and the difference between objective improvements versus improvements in user experience.
The course consists of two main parts: one part with lectures in which we cover several examples from the (popular) literature and consider their merits in the knowledge economy in more detail. The course teachers and guest speakers will present example business cases where data-scientific methods were implemented, considered but not implemented, or not (yet) considered.
The second part consists of analyzing a case: student teams will perform actual data crunching on a real life example and come up with an implementation strategy that takes the potential hurdles and pitfalls as covered in the course into account, with an emphasis on user benefits.
- (Excerpts from) popular scientific books (“Supercrunchers”, “Freakonomics”, “The Signal and the Noise”, etc) and the journal papers that form their academic background.
knowledge of statistics and data mining