Decoding Data Science Upskilling: Insights From 5 Years of Data Science Projects at the Centers for Disease Control and Prevention, 2019-2023.
Context: Public health organizations are increasingly recognizing the value and potential of data science. However, a gap remains in understanding how data science is being applied in public health. Objective: This article provides a comprehensive overview of data science applications in real-world...
| Published in: | Journal of Public Health Management & Practice Vol. 32; no. 2; pp. 260 - 268 |
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| Main Authors: | , , , , |
| Format: | research tables/charts Journal Article |
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Lippincott Williams & Wilkins
Mar/Apr2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191108263&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191108263 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10784659 0O3 jtl: Journal of Public Health Management & Practice issn: 10784659 maglogo: N pubinfo: dt: Mar/Apr2026 vid: 32 iid: 2 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 191108263 191108263 191108263 10.1097/PHH.0000000000002284 191108263 ppf: 260 ppct: 8 formats: tig: atl: Decoding Data Science Upskilling: Insights From 5 Years of Data Science Projects at the Centers for Disease Control and Prevention, 2019-2023. aug: au: Antoine, Mayer Ojo, Adebowale I. Bertulfo, Mary Catherine Okomo-Adhiambo, Margaret Kirkcaldy, Robert D. affil: Author Affiliations: Division of Workforce Development, National Center for State, Tribal, Local, and Territorial Public Health Infrastructure and Workforce (NCSTLTPHIW), Centers for Disease Control and Prevention (CDC), Atlanta, Georgia (Mr Antoine, Drs Ojo, Bertulfo, Okomo-Adhiambo, and Kirkcaldy), and United States Public Health Service, Rockville, Maryland (Dr Kirkcaldy). sug: subj: Coding Data Science Methods Public Health Trends Public Health Methods Workforce Centers for Disease Control and Prevention (U.S.) Professional Development Federal Government Human Funding Source Descriptive Statistics Regression Data Analysis Software Programming Languages Machine Learning Algorithms Artificial Intelligence Decision Making Support, Psychosocial Data Management ab: Context: Public health organizations are increasingly recognizing the value and potential of data science. However, a gap remains in understanding how data science is being applied in public health. Objective: This article provides a comprehensive overview of data science applications in real-world public health settings. By describing the characteristics of projects supported by the Centers for Disease Control and Prevention's Data Science Upskilling (DSU) program during 2019-2023, we seek to guide future efforts in public health data science workforce development and data modernization. Methods: We manually reviewed DSU applications and final presentations about the projects compiled during 2019-2023. We analyzed projects based on 7 characteristics, including public health domain and task, data science topic and method, data modality, tools, and programming languages used. Results: DSU supported 112 data science projects across 5 annual cohorts (2019-2023). Many projects addressed the COVID-19 pandemic (13%), infectious diseases (13%), and vaccines (11%). Approximately half the projects used data visualization (54%) and statistics (51%), with 42% employing artificial intelligence (AI) and machine learning (ML). Furthermore, 52% of projects were designed to support decision making, and 22% sought to improve processes and programs. Learners primarily used RStudio (50%), Jupyter Notebooks (41%), and Power BI (26%), along with Python (56%) and R (55%). AI and ML use increased from 33% of projects in 2019 to 56% in 2023, demonstrating an evolving focus on advanced methodologies. Conclusions: Many teams prioritized data visualization, such as dashboards and visualization tools to support decision making, indicating opportunities for additional infrastructure and training in this area. We observed increasing use of AI and ML, suggesting a need for staff upskilling in these domains. Optimally leveraging data science technologies will require workforce development strategies and data modernization efforts to keep pace with the rapidly evolving field. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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