Course description

This online course, Public Health Informatics (PHI), trains public-health professionals, data scientists, and policy makers to apply information science and modern technologies to protect and improve population health. You’ll learn how surveillance systems are designed and implemented, how to integrate and clean community-level datasets, and how to apply descriptive and predictive analytics (including basic machine learning and GIS) to detect and respond to outbreaks and public-health threats.

The course blends conceptual foundations with hands-on practice: short lectures, guided labs using real or synthetic public-health datasets, dashboarding and mapping exercises, and a capstone project where you design a surveillance/response solution for a realistic scenario. Topics include surveillance system architecture, standards and interoperability (HL7/FHIR basics), data governance and ethics, outbreak detection algorithms, spatial analysis, and the role of PHI in policy and emergency response.

Format: 8–12 weeks (self-paced or instructor-led options), weekly modules, quizzes, practical labs, and a final capstone. Ideal for early-career epidemiologists, program managers, health informaticians, and technologists working with government or NGO public-health programs.

What will i learn?

• Explain the scope and purpose of public health informatics and differentiate it from clinical informatics.
• Identify common population-level data sources and describe standard terminologies and interoperability requirements.
• Design a basic disease surveillance system suited to a defined population and health priority.
• Apply data-cleaning and ETL practices to public-health datasets and evaluate data quality metrics (timeliness, completeness, validity).
• Use descriptive statistics and time-series techniques to detect aberrations and early signals of outbreaks.
• Build simple predictive models (classification/time-series) and interpret model outputs for public-health decision making.
• Create effective dashboards and maps that communicate population health trends and guide interventions.
• Describe legal, ethical, and equity issues in population data use and propose data governance approaches.
• Produce a project plan or prototype (including technical and policy elements) for a surveillance/response intervention.

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