Developing State-of-the-Art Skills

With the development of new data information systems comes the need to help people learn how to use them. As CDC advances modernization efforts, we’re expanding opportunities for staff to gain skills they can use to design, implement, sustain, and innovate with data.

Upskilling means improving the knowledge and abilities of our existing CDC workforce. It’s an important part of CDC’s overall workforce strategy that also includes recruitment, reskilling (learning new skills within a role), and retention of CDC staff with data science and other data-oriented skills.

Led by CDC’s Division of Workforce Development, the Data Science Upskilling (DSU) program offers a team-based, “learning by doing” approach that enables CDC staff and fellows to develop and use their data science skills while working to address the nation’s most pressing health priorities. Participating teams gain access to a subject matter network in data science, peer support through a learning community, and curated online learning resources.

Learning by doing…. together

In July 2022, a total of 25 DSU teams gathered to showcase how they used data science skills in predictive analytics, data reporting, artificial intelligence, and machine learning to solve complex public health challenges. For example:

• Environmental health: Teams from the National Center for Environmental Health used new data visualization tools to evaluate and communicate environmental hazards posed by droughts, fires, and algae blooms.
• Global health: Teams from the Center for Global Health used Python to develop program assessment tools and improve program planning for their many global initiatives.
• Occupational health: Teams from the National Institute for Occupational Safety and Health used machine learning techniques to improve early detection of lung problems and optimize data reporting systems for firefighters.

Overall, this year’s teams tackled a broad variety of urgent public health challenges, such as evaluating rural health and access to care, providing local health departments with new visualizations of viral hepatitis data, evaluating mental health and stress levels in West Virginia’s 3^rd graders, and looking at the intersection of COVID-19 and health equity.

Building on the ideas

The promise and potential of DSU continues to light the way for our staff, showing how we can work together to create and share modern, integrated, and high-quality information that protects the nation’s health.

It’s also lighting the path for our partners to gain new skills. Built from the same ideas, the Council of State and Territorial Epidemiologists’ Data Science Team Training Program is a team-based, on-the job training program to promote data science upskilling at state, territorial, local, and tribal (STLT) public health agencies. Participants in the 12-month program work collaboratively on a project that addresses a current need related to DMI.

Participants upgrade their skills in data science through online courses, working on priority STLT projects, peer-to-peer learning, and coaching from subject matter experts. Importantly, they are able to direct their own learning according to their interests and project needs.

The first class, comprising 20 teams and 92 learners from a mix of state, local, tribal, and territorial health departments, began program activities in January 2021. In December 2022, the second DSTT class, which included 26 teams and 118 learners, delivered final presentations on their experiences.

As just one example, a team from the New York State Department of Health applied the flexible funding provided to DSTT teams to take an intensive training in R. The team then used SaTScan to flag case increases and developed R markdown reports for shigellosis and giardia. Using these methods allows them to produce robust cluster reports at the click of a button. The team plans to continue this work by expanding the reporting capabilities to additional enteric diseases, demonstrating value for the future.

In January 2023, the third class of DSTT will bring together 25 additional teams with 115 new learners. These teams will continue to accelerate real-world progress toward data modernization priorities.