Data science literacy: Toward a philosophy of accessible and adaptable data science skill development in public administration programs

被引:17
作者
Overton, Michael [1 ]
Kleinschmit, Stephen [2 ]
机构
[1] Univ Idaho, Moscow, ID 83844 USA
[2] Univ Illinois, Chicago, IL USA
关键词
data science; data literacy; data-skills gap; research methods; statistics; BIG DATA; CURRICULUM;
D O I
10.1177/01447394211004990
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
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.
引用
收藏
页码:354 / 365
页数:12
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