Are U.S. graduate curricula ready to prepare social data scientists for the AI era? - Dong, Yixiao; Baral, Deodatta; Baral, Kushmakar (Frontiers in Education, 2026)
The evolving skill demands of the data science workforce present unique challenges for individuals trained in the social science disciplines. This study examines the readiness of U.S. graduate programs in preparing social data scientists for the AI era. We collected and analyzed publicly available coursework plans (n = 97) from graduate programs at research universities in the U.S. that focus on training social data scientists. Required skills for data scientists were identified through a random sample of current job postings (n = 30) on LinkedIn and cross-validated with findings from the relevant literature. Using Python-based web scraping and text content analysis, we identified the 10 most in-demand skills within the data science industry and conducted a binary coding of whether each program offers coursework relevant to these skills. These 10 binary indicators were subsequently analyzed using Rasch modeling. The results indicate notable gaps between graduate curricula and industry expectations, and also highlight the need to reform graduate education to better prepare social data scientists for the new demands of the AI era.