Three-Day Workshop on Reproducible and AI-aided Health Data Analysis at IQRAA
A multi-day data analysis workshop series in R, including an AI-basics module and a hands-on EDA module using tidyverse verbs on a WHO tuberculosis dataset.
Pre-Conference Workshop on Infectious Disease Modeling (IDM)
A hands-on, R-based pre-conference workshop on infectious disease modeling, built around collaborative group modeling activities at JHAPSMCON-2026.
Spatial Epidemiology using R
A structured 4-day hands-on workshop on spatial epidemiology in R, covering spatial concepts, map-making, exploratory spatial data analysis, clustering, and interactive visualisation, with a participant handbook.
Health Technology Assessment using R
A hands-on workshop on conducting health technology assessment in R, covering decision-analytic and economic modelling. Participants learn to build decision trees and Markov models, run probabilistic sensitivity analyses, and produce reproducible cost-effectiveness outputs.
R Workshop (BioEngineeR)
An introductory hands-on R and Quarto session that walks participants through building their first Quarto report and rendering it to HTML, Word, and PDF.
AI in Statistical Analysis
An interactive Quarto Reveal.js workshop on applying AI tools to statistical analysis, with ~27 ggiraph-powered slides and a presenter guide.
Data Management Training-of-Trainers (DMgt Asia)
A training-of-trainers workshop covering data management, AI in statistical analysis, and data-science best practices, with group activities and worked solutions.
National Workshop on Health Data Analytics using R
A 3-day national hands-on workshop on health data analytics using R, combining concepts with coding. Cohort 2, delivered at AIIMS Mangalagiri for clinicians and public-health professionals.
GIS & Spatial Data Science for Public Health
A Quarto-based course teaching GIS and spatial data science for public health using R, from spatial data handling to choropleths and hotspot analysis.
GIS for Epidemiology
A Quarto website course on spatial data handling, mapping disease data, spatial autocorrelation, and cluster detection in R for epidemiologists.