Public Health Data Analytics Student

Applicants will be considered based on their knowledge, skills or abilities related to project needs. Specifically, applicants should have experience working with data handling. and analyses, including proficiency in statistical coding languages such as Python. Applicants with experience using AI tools and machine learning approaches on large, complex databases are preferred, but not required. Make an impact while you learn. The Semester of Service Program offers students a volunteer project-based opportunity to support real Federal missions, gaining hands-on experience and career-ready skills. Students must be enrolled at least half-time in an accredited trade school, technical/vocational institute, junior college, college, university, or other accredited educational institution. With this project, you'll use data analytics and machine learning to advance food safety.

• Extract the detailed virulence factors (VF) profiles from the available strains (potentially along with antimicrobial and heavy-metal resistance genes) in the NIH-National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) system.
• Collect the corresponding metadata, including serotype, source, date of isolation, geography, etc. and link the metadata to the VF profiles.
• Conduct VF trend analyses describing how virulence profiles vary across source, time, and geography.
• Identify specific VFs, or groups of VFs, that are more highly associated with more severe human health outcomes.
• Provide evidence-based information to support regulatory decisions on the use of VF data for improving public health.
• Leverage publicly available data from the NCBI-PD Isolate Browser, coupled with advanced data analytics and machine learning to complete the noted aims of the study and gain a very valuable set of skills to aid in their future career efforts.
• Provide a report and give a presentation to agency senior officials.