Student and instructor perceptions of data science integration into science and engineering courses

Data science literacy is vital for undergraduate engineering and science students, yet questions remain about effective integration in curricula. This study investigates the impact of integrating discipline-specific data science modules into existing undergraduate STEM courses at three US universities through a research-practice partnership. Using mixed methods to analyze survey responses from 877 students and instructor grades and interviews across six courses, we examined changes in student data science perception across various demographics, academic levels, and disciplines and compared student and instructor perspective. Results show significant increases in student self-reported perception after completing one or more modules irrespective of course and institution differences. Analysis revealed alignment between student self-assessments and instructor evaluations. Students highlighted benefits including real-world applications and career relevance, while identifying challenges with data analysis tools and varying experience levels. These findings provide insights for educators seeking to integrate data science into curricula.