Overview
Objectives
- Develop practical skills in analysing health data using R, AI coding agents, and reproducible workflows
- Select and apply descriptive and inferential methods to relevant public health questions
- Assess and use AI tools, including LLMs, VLMs, and other machinelearning methods, appropriately and responsibly in health research
- Present findings clearly through visualisations, reports, dashboards, and oral presentations
- Learn alongside an international cohort, exchanging practical experience and perspectives from different professional and geographical settings
Audience
Learning outcomes
Upon completion, participants will be able to:
- Analyse health data using R, AI coding agents, and reproducible workflows
- Clean, manage, and explore complex health datasets for research and practice
- Apply descriptive and inferential statistical methods to health research questions
- Integrate applied AI tools (LLMs, VLMs, and other ML tools) responsibly into health research workflows
- Evaluate the strengths and limitations of analytical and AI-assisted methods in health contexts
- Communicate evidence through clear visualisations, reports, dashboards, and presentations
- Transfer acquired skills to new datasets, tools, and professional settings
Programme
3 modules:
- Introduction to Data Analysis with R for Public Health
- Statistics for Health Research with R
- Applied AI for Health Data
Diploma awarded
Registration
Registration deadline
Fees
CHF 5,500.-
Admission criteria
Candidates must:
- hold a university bachelor's, master’s or equivalent diploma in a relevant field (or a foreign qualification judged equivalent by the Steering Committee)
- demonstrate relevant professional experience; and attest to English proficiency at CEFR level B2 or above
Admission decisions are made by the Steering Committee on the basis of the submitted application. An interview may be requested. The Steering Committee may admit candidates not meeting these criteria after review of their file, possibly including an interview.
Cancellation Policy
Withdrawals prior to the start of the training programme will be subject to a handling fee of CHF 400.-.
Curriculum
Period
Credits
From routine surveillance to clinical research and programme evaluation, modern health systems rely on rigorous analysis of complex data. Yet many professionals enter the field with limited training in the computational and statistical methods needed to synthesise and analyse health data. As AI tools become more widely used in the health sciences, there is also a growing need for training in how to apply them effectively and responsibly.
This CAS provides an accredited pathway to develop and demonstrate those skills. Across three modules, participants build competence in programming, statistical analysis, and the responsible use of AI in health research and practice. Through interactive workshops, assessed projects, and self-paced study, learners work on practical health data problems and gain the experience and confidence to apply health data science in their own professional contexts.
Planning
12 weeks
Description
Covers R and RStudio basics, data manipulation with the tidyverse, visualisation with ggplot2, reproducible reporting with R Markdown, version control with Git and GitHub, interactive dashboards with Quarto, using AI coding assistants, and practical applications using real public health datasets. Assessment includes practical assignments, online quizzes, a module final project.
Planning
12 weeks
Description
Covers descriptive and inferential statistics, simple and multivariable linear and logistic regression, model diagnostics, data cleaning and advanced manipulation, geospatial visualisation, and critical analysis of statistical methods in research papers.
Planning
12 weeks
Description
Covers generative AI and large language models (LLMs), LLM-powered data-analysis tools, text processing and research synthesis with AI, custom AI workflows for health research, responsible and ethical AI use, and image and text AI models for health communication and outreach.
Director(s)
Prof Olivia KEISER, Institute of Global Health, Faculty of Medicine, University of Geneva
Coordinator(s)
Steering committee
- Prof Olivia KEISER, Institute of Global Health, Faculty of Medicine, University of Geneva
- Prof Nicolas RAY, Institute of Global Health, Faculty of Medicine, University of Geneva
- Dr Andrew AZMAN, Institute of Global Health, Faculty of Medicine, University of Geneva
- Prof Douglas TEODORO, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva
- Prof Clémentine ROSSIER, Institute of Demography & Socioeconomics, University of Geneva
- Prof Jennifer CARTER, Nuffield Department of Population Health and Big Data Institute, University of Oxford
Contribution to the Sustainable Development Goals
