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.
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.
A hands-on, R-based pre-conference workshop on infectious disease modeling, built around collaborative group modeling activities at JHAPSMCON-2026.
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.
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.
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.
A teaching module introducing health data science with R and the tidyverse, featuring exercises, group activities, and a worked COVID-19 Kerala data analysis.
An interactive Quarto Reveal.js workshop on applying AI tools to statistical analysis, with ~27 ggiraph-powered slides and a presenter guide.
A training-of-trainers workshop covering data management, AI in statistical analysis, and data-science best practices, with group activities and worked solutions.
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.
A Quarto-based course teaching GIS and spatial data science for public health using R, from spatial data handling to choropleths and hotspot analysis.
A Quarto website course on spatial data handling, mapping disease data, spatial autocorrelation, and cluster detection in R for epidemiologists.