Abstract

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.

Keywords: data science, doctoral education, masters education, interdisciplinary, Capstone, collaborative research

08/21/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©2024 Doreet Preiss, Jessica Sperling, Ryan Huang, Kyle Bradbury, Thomas Nechyba, Robert Calderbank, Gregory Herschlag, and Jana Schaich Borg. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.