Several faculty members have contributed to developing 15.060 Data, Models, and Decisions. In the following pages, Professors Robert Freund and Georgia Perakis discuss their experiences shaping and teaching this core MBA course.
In this section, Prof. Robert Freund discusses important factors his group considered when designing 15.060 Data, Models, and Decisions.
One might think that support for developing a course with the name Data, Models, and Decisions at MIT Sloan would be a no-brainer - after all, we're at MIT! But in the nineties, when the course was developed, there had been a huge backlash against quantitative management curricula in general. And MIT Sloan was no less anti-quant than were our peer schools around the country. So against this backdrop, we felt we wanted to not only deliver a state-of-the-art quantitative curriculum, but we also wanted to ensure that students saw and understood the managerial value of the quantitative concepts. In short we wanted the students to experience the value of quantitative modeling both for their future employers as well as for their own professional development. So the twin pillars of the course were (i) the quantitative content, and (ii) the applications orientation and cases wherein the students would also come to understand the practical value of the quantitative content.
In designing the course, we focused on those technical tools and the cogent examples of their managerial use that we wanted students to retain five years after taking the course
—Robert Freund
We also focused on integrating the design of the course with the MBA program and its own goals. For example, it is important that MBA students get great summer jobs in the summer between the two years of their program. Summer job interviews occur in January—after our course is completed. The course serves and helps the MBA program by delivering the kind of course content that the students can use in their summer job search and in their summer job interviews. So this was a specific design goal of the course.
We also wanted the course to stand the test of time with the students. We wanted the students to have the important underlying concepts significantly rooted so that recall would not be difficult several years later—maybe not recall of exotic formulas, but recall of the key concepts by which one does quantitative modeling and thinking. And after all, one can always look up the formulas in a textbook or online. So in designing the course, we focused on those technical tools and the cogent examples of their managerial use that we wanted students to retain five years after taking the course.
It’s also important that students simply enjoy the course, not just because that’s always a good thing, but also because the course is required and covers topics that they would not necessarily go out their way to learn if not for the requirement. We had to think about the fact that we were requiring them to take 15.060 Data, Models, and Decisions at the expense of other courses they might wish to be taking, and we wanted them to be happy afterwards for having taken the course.
We also wanted the Sloan faculty outside of our group to be supportive of the course. So one of the things we did in designing the course was to ask the different faculty groups at Sloan what quantitative concepts they wanted students to know that we could teach them in our course, to help the students be better prepared for their courses in their fields.
Finally, and just as importantly, the course needed to be designed so that the faculty would really enjoy teaching it. The challenge here is putting together a curriculum that ten or twelve different faculty can successfully teach, and not just the exceptional colleagues who have won teaching awards. We couldn’t just choose content because it happened to be our own specific favorite material; and we had to make the presentations as un-stylized as possible, while keeping them dynamic and fun—in order to accommodate the different teaching styles of the different faculty instructors.