Interdisciplinarity in Data Science Pedagogy: A Foundational Design
Find this in a Library
Data science is an interdisciplinary field that generates insights in data to aid decision-making. Recognizing that data scientists must be interdisciplinary, agile, and able to adapt to data analysis across many domains, both academia and the industry are striving to integrate interdisciplinary learning and transferable skills into data science curriculum. This paper introduces an interdisciplinary approach to teaching the foundations of data science. We evaluate two different interdisciplinary formats. The first format considers collaborative efforts among instructors with different academic disciplines. The second involves a sole instructor that discusses data science concepts from different disciplines and related to business processes, computer science, and programming. We demonstrate that interdisciplinarity ensures favorable learning experiences and produces high learning outcomes. We also show that our course design maintains and promotes interdisciplinarity even in situations where logistical constraints would not support the use of multiple instructors to deliver one course.
Asamoah, D. A.,
& Schiller, S.
(2018). Interdisciplinarity in Data Science Pedagogy: A Foundational Design. Journal of Computer Information Systems.