Broadening Data Science Education: An Experience Report

Data Science is an essential concept for the twenty-first-century workforce. As a result, the need to incorporate core data-intensive skills into nearly every discipline has recently gained increased attention. This paper details our experiences developing a framework where CS and non-CS faculties collaborate on developing contextual data science modules tailored to the needs of multiple disciplines, academic levels, and student and instructor preparedness and integrate them into existing undergraduate courses. A quantitative and qualitative analysis approach is used to explore the following research question: To what extent a common framework be utilized to integrate data science-based learning objectives into multiple science, social, and health science courses to generate more data science-informed graduates? A significant number of underrepresented and minority (URM) students were exposed to data science knowledge and skills throughout this intervention during the last three years. The student performance and survey results show that most students understood the concepts and could use related tools to organize and use data to support their claims and conclusions to a certain extent. Further analysis of instructor perspectives identified commonalities, such as the benefits of incorporating data science concepts into disciplinary contexts through hands-on exercises using real-world data, as well as differences, such as student activity types based on instructor and student preparedness and usage and rigor of data science tools.