Teaching data science workshops in academic libraries: Insights from attendance patterns and topic preferences

As data science becomes increasingly essential across academic disciplines, academic libraries have emerged as key providers of data literacy and research computing training. This study analyzes attendance data from data science workshops offered by our university libraries for graduate students to understand participation patterns, topic preferences, and audience needs. Results show that workshops focused on data visualization and introductory machine learning attracted the highest attendance across both STEM and non-STEM fields. While advanced machine learning sessions had smaller size attendance, they drew sustained interest from research-oriented participants such as PhD students and postdoctoral researchers. Attendance in programming workshops varied by language, with R and Python emerging as the most popular options. These findings highlight the importance of maintaining a balanced workshop portfolio that supports both foundational and advanced learners. The analysis also reveals opportunities to improve accessibility through standalone design and flexible delivery formats that accommodate diverse schedules and learning backgrounds. Overall, this study provides actionable insights into how academic libraries can better support graduate-level data science education and serve as a model for similar initiatives at other institutions.