Teaching the Foundations of Data Science: An Interdisciplinary Approach

The astronomical growth of data has necessitated the need for educating well-qualified data scientists to derive deep insights from large and complex data sets generated by organizations. In this paper, we present our interdisciplinary approach and experiences in teaching a Data Science course, the first of its kind offered at the Wright State University. Two faculty members from the Management Information Systems (MIS) and Computer Science (CS) departments designed and co-taught the course with perspectives from their previous research and teaching experiences. Students in the class had mix backgrounds with mainly MIS and CS majors. Students’ learning outcomes and post course survey responses suggested that the course delivered a broad overview of data science as desired, and that students worked synergistically with those of different majors in collaborative lab assignments and in a semester long project. The interdisciplinary pedagogy helped build collaboration and create satisfaction among learners.

💡 Research Summary

The paper addresses the growing demand for data‑science professionals by documenting an interdisciplinary approach to teaching a foundational Data Science course at Wright State University, the first of its kind at the institution. Two faculty members—one from Management Information Systems (MIS) and one from Computer Science (CS)—collaboratively designed the syllabus, aligning the six canonical stages of data‑science work (data acquisition, cleaning, exploration, visualization, modeling, and interpretation) with the distinct expertise of each discipline. Lectures combined theoretical foundations (statistics, database concepts, programming, machine learning basics) with practical labs that used real‑world corporate datasets. The labs emphasized hands‑on experience in Python and R, guiding students through end‑to‑end workflows from raw data to actionable insights.

A central feature of the course was the mandatory formation of mixed‑major teams for a semester‑long project. Team roles were deliberately diversified—data engineering, analytical modeling, and business‑impact articulation—to ensure that MIS students contributed domain knowledge and data‑governance perspectives while CS students focused on algorithmic implementation and computational efficiency. Assessment blended quizzes, individual assignments, project deliverables, and peer‑evaluation scores, the latter quantifying each member’s collaborative contribution and reinforcing the interdisciplinary learning objectives.

Post‑course surveys indicated high levels of satisfaction, with students reporting improved ability to work across disciplinary boundaries, greater confidence in handling authentic data, and a stronger appreciation for the synergistic value of combining MIS and CS viewpoints. Faculty observations corroborated these findings, noting enhanced critical‑thinking, communication, and problem‑solving skills among participants. The authors conclude that an explicitly interdisciplinary curriculum—structured around complementary expertise, hands‑on labs, and collaborative projects—significantly boosts learning outcomes and prepares students for the multifaceted demands of modern data‑driven organizations. They recommend further studies to compare similar models across institutions, track long‑term career impacts, and refine the balance of disciplinary content for broader applicability.