Data associated with "Student and Instructor Perceptions of Data Science Integration into Science and Engineering Courses"

Data science literacy is increasingly vital for undergraduate engineering and science students, yet questions remain about effective integration approaches across established curricula. This study presents a case study investigating the impact of integrating discipline-specific data science modules into existing undergraduate STEM courses at three different universities in the United States (US) through a multi-university research-practice partnership examining both student perspectives and instructor course assessments. Using mixed methods analysis of survey responses from 877 students and instructors' grades and interviews across six courses, we examined changes in students' perceptions of data science across various demographics, academic levels, and disciplines and compared student and instructor perspectives. Results show significant increases in students' self-reported motivation, skills, interest, and confidence after completing one or more modules, with initial perception being the strongest predictor of final perception after controlling for course and institution differences. Analysis revealed general alignment between student self-assessments and instructor evaluations. Students highlighted benefits including real-world applications and career relevance, while identifying challenges with data science tools and varying experience levels. These findings provide insights for engineering educators seeking to integrate data science into their curricula.