Online Program

Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years (308234)

Catherine Baker, Creighton University 
Rebecca Gasper, Creighton University 
*Aimee Schwab-McCoy, Creighton University 

Keywords: Data science education, curriculum, computing

As data science matures as a discipline, there has been movement toward developing a consensus on what data science is, and what should be taught in data science courses. However, the diversity of course and program content still presents a challenge for data science faculty from all disciplines. This work identifies some of the most pressing needs for developing data science into a unified curriculum, and suggests directions for research and pedagogical development moving forward. In fall 2019, 69 data science instructors at the tertiary-level were invited to take a survey about their data science courses and programs. Faculty who have taught an introductory data science course at least once, or would be teaching it in the near future, were eligible to participate. Faculty were asked about their background and experience teaching data science, the primary audience for data science at their institution, the challenges or obstacles they faced when developing their courses, their approach to assessment in data science, and what resources could help them become a better data science instructor. Additionally, faculty were asked to select from a list of 34 options which topics were covered in their introductory courses, elsewhere in their program, or omitted. The list of topics was selected based on competency areas and curriculum recommendations in the EDISON Data Science Framework, Curriculum Guidelines for Undergraduate Programs in Data Science, and the ACM Task Force on Data Science Education Draft Report. Despite recent curricular recommendations, there is no clear consensus on what data science courses or programs should contain. While that speaks to the inherent interdisciplinarity of data science, it presents a challenge for new faculty and administrators. Addressing the challenges data science faculty have identified by developing more teaching resources, building shared curricula, and providing faculty training will be a major step toward mature data science.