Most pertinent to the present study is the course’s case revolving around epidemiology. This case presents a large, complex problem: how do we prepare for the seasonal flu and be ready for the next pandemic? This problem is broken down into a more specific sub-problem: how do we predict the number of people who will get infected each year? The expert who presents this case—an associate professor of epidemiology—further decomposes this question by walking learners through an algorithm that centers around four categories of people: vaccinated, susceptible, infected, and recovered. Finally, a series of computational modeling tools specific to epidemiology are presented for learners interested in diving more deeply into this problem.