The proliferation of vast quantities of available datasets that are large and complex has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. Nowadays, this training starts in an introductory data science course at most institutions. The demand for seats in such courses from students has also been increasing equally steadily over the past decade. In this talk, we present a case study of an introductory undergraduate course in data science and statistical thinking that is designed to address these needs and that has scaled from serving 18 students to over 300 students each semester over the last nine years.

January 12, 2024