Course details
Tutors
Aims
This course aims to:
- provide you with a practical, hands-on introduction to data analysis and statistics using R, building confidence through guided coding exercises and real-world examples
- equip you with the skills to build, check, and interpret linear models, the foundation of statistical analysis across disciplines
- empower you to create clear, reproducible reports using Quarto, enabling you to communicate your findings effectively and continue your learning independently
Course content
Statistics can feel intimidating, but it doesn’t have to be this way. This course offers a welcoming, practical introduction to data analysis using R, designed for learners with no prior coding experience. You will discover that the logic behind statistical methods is more accessible than it first appears, and that with the right guidance, anyone can learn to work confidently with data.
You will begin by getting comfortable with R and RStudio, learning how to load, explore, and visualise data. From there, you will build linear models, the workhorses of statistical analysis that underpin much of the research you encounter in science, social science, medicine, and beyond. You will learn not just how to run these models, but how to understand what they tell you and, crucially, how to check whether you can trust them.
The course places strong emphasis on interpretation and communication. You will learn to move beyond p-values to consider effect sizes, confidence intervals, and what your results actually mean in practice. Using Quarto, you will learn to create polished, reproducible reports that bring together your code, visualisations, and narrative, skills that are increasingly valued across research and professional contexts.
Throughout the week, you will work with real datasets, troubleshoot common problems, and develop the practical judgment that comes from hands-on experience. By the final day, you will have completed an analysis from start to finish and gained a clear understanding of where to take your learning next.
What to expect on this course
This is a hands-on, in-person course designed to make statistics and coding accessible through guided practice and supportive instruction. Each session combines live coding demonstrations with opportunities for you to work through exercises at your own pace, building skills progressively across the week.
You will code alongside the instructor, learning by doing rather than by passive observation. Sessions are structured to allow plenty of time for questions, experimentation, and troubleshooting. You are encouraged to make mistakes; they are often the best way to learn, and you will be supported in working through them.
The course is designed for beginners. Whether you have never written a line of code or have dabbled but felt lost, the sessions will meet you where you are. Concepts are introduced with clear explanations and visual demonstrations before you apply them yourself. You will work with real datasets that illustrate the kinds of questions researchers actually ask, making the learning immediately relevant.
The atmosphere is collaborative and welcoming. Questions are not just tolerated but actively encouraged. If something is unclear, chances are others are wondering the same thing. By the end of each day, you will have concrete skills you can apply, and by the end of the week, you will have the confidence to continue learning independently.
You will need to bring your own laptop with R and RStudio installed. Instructions for setup will be provided before the course begins.
Course sessions
Day 1: Getting Started with R and Your Data
Making friends with R and RStudio
- Tour of R and RStudio: a welcoming introduction
- Understanding your data: types, structures, and how to look at it
- Loading data and creating your first Quarto document
- Exploratory data analysis: pictures that tell stories
- Summarising data in ways that make sense
- The bigger picture: from your data to understanding the world
Day 2: Understanding Relationships - Linear Models
The workhorse of data analysis
- What is a linear model, and why should we care?
- Simple relationships: does X predict Y?
- Visualising relationships in your data
- Understanding what the numbers actually mean
- Multiple predictors: when one variable isn't enough
- Hands-on: Building your first models together
Day 3: Can My Model Be Trusted? Checking Assumptions
Learning to be a detective with your data
- Why assumptions matter (without the scary maths)
- Visual tools for checking your model: plots that tell you what's wrong
- The key things to look for and what they mean
- When things go wrong: practical solutions
- Knowing when to trust your results
- Working through real examples together
Day 4: What Does It All Mean? Interpretation and Communication
Making sense of your results
- Understanding statistical significance (and why it's not everything)
- Confidence intervals: embracing uncertainty
- Effect sizes: what matters in the real world
- Comparing models: which one fits best?
- Creating clear, beautiful visualisations with ggplot2
- Making tables and reports that people actually want to read
Day 5: Bringing It All Together
Your complete workflow and next steps
- Walkthrough: A complete analysis from start to finish
- Building a polished Quarto report with your findings
- Troubleshooting common errors and where to find help
- Best practices: organising your code and data
- Brief tour of the statistical landscape: What else exists (mixed models, GLMs, machine learning) and when you might need them
- Final Q&A and celebration of what you've learned!
Learning outcomes
As a result of the course, you will gain a greater understanding of the subject and you should be able to:
- use R and RStudio to load, explore, and visualise data, applying exploratory techniques to understand the structure and patterns within a dataset
- build and interpret linear models, including models with multiple predictors, and critically evaluate whether model assumptions are met using diagnostic tools
- produce clear, reproducible reports using Quarto that integrate code, visualisations, and written interpretation to communicate statistical findings effectively
Required reading
Before the course begins, please install the following free software on your laptop:
- R (the statistical programming language): https://cran.r-project.org
- RStudio Desktop (the integrated development environment): https://posit.co/download/rstudio-desktop/
- Quarto (for creating reproducible reports): https://quarto.org/docs/download/
Detailed installation instructions will be provided before the course.