Evaluation of EDISON's Data Science Competency Framework Through a Comparative Literature Analysis
Foundations of Data Science
During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or course materials. We identify areas of strong agreement and disagreement with the framework. Results from the literature analysis provide strong insights into what topics the broader community see as belonging in (or not in) Data Science, both at curricular and course levels. This analysis can provide important guidance for groups working to formalize the discipline and any college or university looking to build their own undergraduate Data Science degree or programs.
Schmitt, Karl R. B.; Clark, Linda; Kinnaird, Katherine M.; Wertz, Ruth E. H.; and Sandstede, Björn, "Evaluation of EDISON's Data Science Competency Framework Through a Comparative Literature Analysis" (2023). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
Digital Object Identifier (DOI)
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Archived as published. Open access paper.