Where Data Science and the Disciplines Meet: Innovations in Linking Doctoral Students With Masters-Level Data Science Education

Although the need for data science methodological training is widely recognized across many disciplines, data science training is often absent from PhD programs. At the same time, Masters-level data science educational programs have seen incredible growth and investment. In 2018, Duke initiated a National Science Foundation (NSF)-funded program to determine whether Masters-level data science programs that universities have already invested in could be leveraged to reduce data science education barriers doctoral students face. Doctoral Fellows from diverse fields worked with teams of master's students from Duke's Master in Interdisciplinary Data Science program on applied Capstone projects focused on the doctoral Fellows' own disciplines and dissertation research. Fellows also gained access to the Master program's courses and professional development resources. We examined the implementation, experience, and effect of this integration into Master of Data Science program infrastructure using qualitative data collection with doctoral Fellows, master's students, and Fellows' doctoral advisors. Master's students participating in doctoral-led Capstones benefited from their doctoral Fellows' mentorship, project management, and content knowledge. Participating doctoral students showed increased learning of data science techniques and professional skills development. While some Fellows' research was advanced through the Capstones, data also showed mismatches between selected master's program goals and doctoral students' needs. Overall, this pilot indicated potential promise in harnessing existing Master in Data Science programs to bolster doctoral students' data science learning and professional readiness while also identifying areas for improving future such efforts.