1 Grant + 2 Institutions + 3 Course Variations = Data Science 4 All

Abstract

Data Science, often described at the intersection of computer science, statistical thinking and analysis, and subject matter expertise, has seen an exponential growth in the past few years. Courses (and entire programs) have been appearing at such a fast rate at most institutions of higher education, as well as some high schools, that comparisons between curricular and delivery models and rigorous discipline-based education research are often overlooked in order to gain competitive advantages. This study attempts to rectify that absence by evaluating, comparing, and discussing four different courses offered at two different institutions of higher education. Funded by NSF via a collaborative grant (DUE-1432438), faculty from Computer Science and Statistics departments collaborated on the development and evaluation of introductory courses in Data Science for all students, using a discipline-based education research approach. Data on students were gathered including demographics, curriculum, statistical knowledge, and attitudes towards Data Science. Post-course growth was measured, when available, and compared through formal statistical inference. End-of-course evaluations, with supplemental questions about student learning, were reviewed and will be summarized. Finally, reflections on successes, challenges, and lessons learned will be shared.