Training Interdisciplinary Data Science Collaborators: A Comparative Case Study

Data science is inherently collaborative as individuals across fields and sectors use quantitative data to answer relevant questions. As a result, there is a growing body of research regarding how to teach interdisciplinary collaboration skills. However, much of the work evaluating methods of teaching statistics and data science collaboration relies primarily on self-reflection data. Additionally, prior research lacks detailed methods for assessing the quality of collaboration skills. In this case study, we present a method for teaching statistics and data science collaboration, a framework for identifying elements of effective collaboration, and a comparative case study to evaluate the collaboration skills of both a team of students and an experienced collaborator on two components of effective data science collaboration: structuring a collaboration meeting and communicating with a domain expert. Results show that the students could facilitate meetings and communicate comparably well to the experienced collaborator, but that the experienced collaborator was better able to facilitate meetings and communicate to develop strong relationships, an important element for high-quality and long-term collaboration. Further work is needed to generalize these findings to a larger population, but these results begin to inform the field regarding effective ways to teach specific data science collaboration skills.