Bringing Interdisciplinary Data Science Education Challenges into the Classroom

This article examines the growing prevalence and challenges of interdisciplinary data science education (IDSE) in higher education globally. We argue that while data science education has traditionally focused on statistical and computational skills, these emerging challenges require urgent attention as interdisciplinary data science becomes increasingly computational, intelligent, and expansive. Drawing on over 35 years of combined teaching experience across multiple European universities, we identify and analyze four critical areas facing IDSE classrooms, namely: interdisciplinarity; skills differences; decolonization; and ethics. By reflecting on these challenges, we emphasize the urgent need for data science educators to model conscious, critical, and reflexive interdisciplinary data science praxis in an increasingly data-dependent world. This is even more escalated by the fact of a fast-growing development of AI, ML, and political discussion on governance and impact of everyday life. The article concludes by advocating for an international community of data science educators across disciplines to address these challenges collectively.