This intensive 3-day course introduces the statistical software and programming language R with specific focus on its applications in life sciences and clinical research.

Each day of the course focuses on a specific topic, as follows:

Day 1. R fundamentals: An introduction to the language and its interface. Get familiar with R, R studio, operators, variables, functions, directories and the script editor.

Day 2. Data visualisation: Providing you with the tools to make publication quality plots using R.

Day 3. Statistical analysis in R: We will cover statistical methods ranging from simple univariate statistics to dealing with large volumes of variables using Principal Component Analysis.

The course is designed to be undertaken at the student’s own pace with 1 on 1 targeted support from our team of experts. Our team comprises computational biologists, data scientists, and bioinformaticians with a strong background in both wet and dry lab projects. Empowering you on a self-learning path, you will gain essential skills to apply R to your research projects and to generate publication-ready outputs.

We will also be available to assist with your learning up to two months after the final day of the course. Moreover, upon completion of the course, all delegates will be invited to join a peer-peer support community “RClub” to enhance their research/analytical work.

This course runs twice a year – in March and in October. The next cohort will be held online on 6th- 8th March 2024.

Sign up using the website link provided before the 20th February. Places are limited and registration may close earlier if places are full.

The course has a cost of £300 for all academic delegates; £500 for all delegates from public institutions (not academic). If you are a delegate from the University of Liverpool you can access student bursaries, that you can apply to using this link. Note the delegate bursaries applications close on the 12th February 2024. You must submit both a registration form and a bursary application to be considered.

For more advanced data analysis (e.g. omics) and data science projects, we strongly recommend you also sign up for our R for Data Science course.