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Building data science capacity in the public health workforce: a Public Health Data Science Life Cycle framework
Emily M Burke1, Jordan A Fox1, Sarah Gusman1
1Association of Schools and Programs of Public Health, Washington, DC, United States.
Frontiers in Public Health
|August 1, 2026
Summary
Public health systems need data science skills, but training is lacking. This study maps data science competencies to the public health data science lifecycle, identifying gaps to improve workforce development and decision-making.
Area of Science:
- Public Health
- Data Science
- Workforce Development
Background:
- Digital transformation in public health necessitates data science expertise for population health decision-making.
- Current public health workforce development has not adequately integrated data science competencies into training programs.
Purpose of the Study:
- To present a competency mapping framework for public health data science.
- To propose a national training agenda to operationalize data science skills for the public health workforce.
Main Methods:
- Synthesized existing public health competency frameworks, accreditation standards, and workforce analyses.
- Mapped competencies to an eight-stage Public Health Data Science Life Cycle.
- Identified competency gaps and developed new competencies for emerging needs.
Main Results:
- Existing competencies are concentrated in data collection, analysis, and interpretation stages.
- Significant gaps exist in problem framing, communication, data governance, ethical data use, and visualization.
- Competencies are lacking in earlier and later stages of the data science lifecycle.
Conclusions:
- A framework is proposed to integrate public health data science competencies into training and professional development.
- Aligning competencies with the full data science lifecycle enhances workforce development.
- The goal is to strengthen data-informed decision-making and improve population health outcomes.
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