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.

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Mass adoption of advanced information technologies is fueling a need for public servants with the skills to manage data-driven public agencies. Public employees typically acquire data skills through graduate research methods courses, which focus primarily on research design and statistical analysis. What data skills are currently taught, and what content should Master of Public Administration (MPA) programs include in their research method courses? We categorized research method course content in 52 syllabi from 31 MPA programs to understand how data skills are taught in public administration. We find that most graduate programs rely on research methods more suited for academic and policy research while lacking the data skills needed to modernize public agencies. Informed by these results, this work presents the Data Science Literacy Framework as a guide for assessing and planning curriculum within MPA programs.

Introduction to the journal from the editors and MPAC president.