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Advanced Epidemiology Pathway

Choose your course track

Explore three focused learning tracks designed for epidemiology, public health research, thesis work, and data-driven community medicine practice.

B

Biostatistics Course

Build a strong foundation in statistical reasoning, hypothesis testing, effect measures, sample size, regression, and interpretation for public health research.

R

Statistical Analysis using R and Python

Develop practical analysis workflows for cleaning data, visualizing results, choosing tests, running models, and presenting findings from health datasets.

G

Spatial Epidemiology and GIS Mapping

Learn disease mapping, spatial patterns, hotspots, GIS workflows, and location-based evidence for outbreak response and community health planning.

Suggested learning flow: begin with Biostatistics, continue with R and Python analysis, and then apply your skills to spatial epidemiology and GIS mapping.