Designing curricula for data science based on fundamental skills and competencies informed by expert interviews

With computational modeling and data analysis skills becoming an increasingly integral part of modern scientific work and research, understanding the specific competencies and skills required for individuals to participate in this work is growing more important. Previous efforts have defined these competencies and skills to varying degrees, with recommendations ranging from writing scientific software to designing entire degree programs in data science; however, the existing computational science education literature is incomplete in some ways. Primarily, much of the existing literature lacks a research-based foundation. We conducted a series of semi-structured interviews with experts in academic and industry settings to broadly describe the skills that students need to have in order to participate in work and research in computational science. In this talk, I will present the results of our research and highlight some of the design choices we have made for our introductory computational science course and how those choices connect to our research findings.