Data science literacy: Toward a philosophy of accessible and adaptable data science skill development in public administration programs
Michael Overton, Stephen Kleinschmit
Abstract
Public administration is struggling to contend with a substantial shift in practice fueled by the accelerating adoption of information technology. New skills, competencies and pedagogies are required by the field to help overcome the data-skills gap. As a means to address these deficiencies, we introduce the Data Science Literacy Framework, a heuristic for incorporating data science principles into public administration programs. The framework suggests that data literacy is the dominant principle underlying a shift in professional practice, accentuated by an understanding of computational science, statistical methodology, and data-adjacent domain knowledge. A combination of new and existing skills meshed into public administration curriculums help implement these principles and advance public administration education.