Abstract
Data science is a newly‐formed and, as yet, loosely‐defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term “data science,” particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective.
Highlights
The major components of data science have existed for many years, the term has rapidly grown in prominence in the last decade
We propose a definition to frame our discussion: “Public health data science is the study of formulating and rigorously answering questions in order to advance health and well-being using a data-centric process that emphasizes clarity, reproducibility, effective communication, and ethical practices.”
We have defined and discussed public health data science, but have not precisely located this field with respect to public health or public health’s constituent disciplines. This reflects current reality–individuals who identify as public health data scientists often do so as a secondary discipline, with primary expertise in epidemiology, biostatistics, health policy, environmental health, or another area
Summary
The major components of data science have existed for many years, the term has rapidly grown in prominence in the last decade. This reflects the confluence of several important trends in science, including the prevalence of big data, the development of computational approaches to analysis, and recognized need for reproducibility in research.
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