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Research Statistics With R:
Intro to Statistical Methods for Health Sciences
Ready to unlock the power of R for Research Statistics?
Our 12-week part-time course connects you with R experts and a supportive community. You’ll build a portfolio of data projects, learn to program with tidyverse packages, and earn a certificate to showcase your R proficiency. The course includes:
- Weekly workshops in a live, engaging classroom
- Personalized feedback and mentorship from experts
- A rigorous statistics curriculum covering descriptive statistics, inferential testing (t-tests, ANOVA, chi-square), and regression modeling (simple, multiple, and logistic)
- Hands-on practice applying statistical methods to real public health data in R
- Dedicated help sessions (two 2-hour sessions per week) for personalized support
- A capstone final project: a full public health data analysis with written paper and R script
- 4 ECTS credits (for partner university students)
Is this course for you?
This course is ideal for researchers, analysts, and professionals in the life sciences, medical sciences, and social sciences who want to apply rigorous statistical methods to real data using R. A basic familiarity with the R programming language is required. If you are looking for a coding-focused introduction to R before diving into statistics, consider taking our R Basics & Beyond course first.
Tuition: $175 per month (x3 months)
Financial hardship scholarships available.
Indicate your need in the standard enrollment form.
We offer a generous 1-month full refund policy so you can try the course risk-free.
Course Structure
12 weeks
Trimester 2: May 21 - Aug 6, 2026
Basic R coding experience
Online classroom
Thursdays 10am GMT (check local time)
Thursdays 3pm GMT (check local time)
Sundays 2pm GMT (check local time)
Hear from GRAPH Graduates
Course Modules
Descriptive Statistics
Build your statistical foundation with R. Explore data types and distributions, visualise health datasets using ggplot2, and calculate key measures of central tendency and dispersion including mean, median, standard deviation, z-scores, and the normal distribution.
DURATION: 1 week
TOPICS
Data types & variable classification, histograms & density curves with ggplot2, mean / median / mode, measures of dispersion, z-scores, normal distribution, binomial & Poisson distributions
Inferential Statistics
Move from describing data to drawing conclusions. Master sampling distributions, the Central Limit Theorem, standard error, and confidence intervals. Then test scientific hypotheses with rigour interpreting p-values, controlling for Type I & II errors, comparing group means with t-tests, running ANOVA across multiple groups, and analysing categorical associations with chi-square tests. All applied to real epidemiological and clinical datasets in R.
DURATION: 2 weeks
Linear Regression
Model and predict continuous health outcomes. Fit simple and multiple linear regression models in R, interpret coefficients and R², check assumptions (linearity, homoscedasticity, normality), manage confounding and multicollinearity, and apply log and polynomial transformations. Produce publication-ready regression tables and visualisations for health research reports.
DURATION: 3 weeks
TOPICS
Simple linear regression, slope & intercept, R², residual diagnostics, homoscedasticity, normality checks, multiple predictors, confounding, multicollinearity, adjusted R², log & polynomial transformations, regression reporting
Logistic Regression
Analyse binary health outcomes such as disease status, treatment response, and mortality risk. Fit simple and multiple logistic regression models in R, interpret odds ratios, evaluate model fit with ROC curves, handle effect modification and rare events, and present adjusted results in research-quality tables suitable for publication.
DURATION: 3 weeks
TOPICS
Binary outcomes, log odds, odds ratios, probability, ROC curves, sensitivity & specificity, AUC, adjusted odds ratios, covariate selection, interactions, effect modification, rare events, logistic regression reporting
Capstone Project
Demonstrate your full statistical expertise on a real public health dataset. Working independently, you will clean and explore data, apply appropriate hypothesis tests, and build regression models then present your findings as a polished written paper with a fully reproducible R script. Projects are presented to peers and evaluated by instructors as a tangible demonstration of competency.
DURATION: 3 weeks
TOPICS
Data cleaning & exploration, exploratory data analysis, hypothesis testing, regression modelling, results interpretation, written public health report, reproducible R script, peer presentation
Our Approach
Supportive Community
Online learning can be isolating. Our live classes and study halls create a vibrant community of aspiring data analysts.
- Rich peer interactions in our videoconferencing classroom
- Online groups, forums, teams: get all the support you need
- Improve your skills with constructive feedback from instructors
– Hello from a recent cohort!
– An example page from a student’s capstone Project
Hands-on Learning
Theory is important, but application is key. Throughout the course, you’ll work on real-world projects that demonstrate your growing skills to potential employers.
- Weekly quizzes and coding challenges to reinforce concepts
- Timely feedback on your work for targeted improvement
- Comprehensive capstone project
Modern Curriculum
There is a lot to learn in the R space. Figuring out what’s essential can be challenging. Our experts have curated a focused curriculum that covers the most important and up-to-date aspects of R for data analysis.
- Modern R libraries, with a focus on the tidyverse
- Reproducible research practices with R Markdown and Quarto
- Learn to use AI tools like ChatGPT to accelerate your coding
– One of our textbooks. Available online at datawithr.com
Course Creators & Tutors
Meet our passionate team of data analysis professionals.
Prof. Olivia Keiser
Course Advisor
Head of the Mathematical Modelling & Infectious Diseases Division, University of Geneva; GRAPH Network Director
Dr Sara Botero Mesa
Course Advisor
Scientific Collaborator at the University of Geneva | COO of the GRAPH Network
Kenechukwu Nwosu
Course creator & Tutor
Research Assistant at the University of Geneva; GRAPH Courses Director
Joy Vaz
Instructional lead
GRAPH Network Training Coordinator and Instructor
Camille Valera
Course Creator & Tutor
Project Manager and Scientific Collaborator, the GRAPH Network
sabina Rodriguez Velásquez
Course Creator & Tutor
Project Manager and Scientific Collaborator, the GRAPH Network
Santiago Sotelo
Course Instructor
Data Scientist at PUCP; GRAPH Courses Tutor
Prof. Flavio Coelho
Course Advisor
Associate professor of Mathematical Epidemiology Getulio Vargas Foundation, Rio de Janeiro; EpiGraphHub Director
Dr. Guy Sadeu Wafeu
Course creator & Tutor
Dept. of Internal Medicine, University of Yaounde; GRAPH Courses Tutor.
