Naseri, Md. Yunus; Snyder, Caitlin; Perez-Rivera, Katherine X.; Bhandari, Sambridhi; Workneh, Habtamu Alemu; Aryal, Niroj; Biswas, Gautam; Henrick, Erin C.; Hotchkiss, Erin R.; Jha, Manoj K.; Jiang, Steven; Kern, Emily C.; Lohani, Vinod K.; Marston, Landon T.; Vanags, Christopher P.; Xia, Kang. “Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership.” IEEE Transactions on Education, vol. 68, no. 1, 2025, pp. 1-12, https://doi.org/10.1109/TE.2024.3436041.
This article explores a collaboration between university instructors who worked together to introduce data science concepts into their courses. Professors from six undergraduate classes at three different universities created data science lessons specifically designed to fit their subject areas, student levels, and teaching styles.
As demand grows for graduates with data science skills, many STEM programs are adding these topics to their courses. However, instructors often struggle to fit data science instruction into their already packed schedules. This study examines how instructors from different engineering and science fields worked together to incorporate data science into their teaching.
Researchers used a case study approach, analyzing how each course integrated data science. Instructors designed their lessons to meet the needs of their specific courses, using them as either a central part of the curriculum or as a supplement. They focused on broad data science skills, such as creating and interpreting visualizations and performing basic statistical analyses.
One challenge instructors faced was the wide range of data science experience among their students. Despite this, they appreciated having control over how they incorporated data science into their courses. While they were unsure if their lessons could be used by other instructors outside their fields, they believed their approach could be adapted to meet student needs in different settings.
