R for data science

This course is intermediate level and builds on the content from our R for beginners course. We cover a wide range of topics from programming concepts to introduction to machine learning. Offered online and can be undertaken at your own pace accessing 1-1 support when required.

Next course: Tues 14th - Fri 17th April 2026. Registration will open a month or so before.

Through hands-on exercises and guided instruction, you will learn to:

  1. Write your own custom functions.
  2. Use for loops, apply() functions and if statements to automate repetitive tasks.
  3. Analyse large matrices of data in a semi-automated and reproducible way.
  4. Apply appropriate data normalisation techniques to omics data.
  5. Quantify batch effects in datasets and apply corrections to mitigate their impact.
  6. Undertake the most common clustering algorithms including k-means and hierarchical.
  7. Perform variable selection and present these results in different plots including heatmaps.
  8. Conduct 2-way ANOVA in R.
  9. Fit and evaluate multivariate models in R for exploratory and predictive analysis.
  10. Explore foundational machine-learning concepts.
  11. Perform functional enrichment analysis in R.

The course includes brief theoretical introductions followed by practical exercises based on real-life research examples. The first two days focus on programming concepts that enable delegates to speed up their analyses and build their own custom functions. The following two days provide practical work covering typical biological data research pipelines, from normalisation to functional enrichment.

Administrative information

  • This course requires prior knowledge of R. Places are extremely limited and will be offered based on the answers from the registration form.
  • Fees are £400 for academic delegates, £600 for delegates from public institutions (not academic). Industry delegates may submit an application and will be quoted on an individual basis.
  • If you are a delegate from the University of Liverpool you can access student bursaries. Please fill in your application form in the link accessible via the registration form. Note the delegate bursaries applications close earlier than general registrations. You must submit both registration and bursary forms together if applying for one.

 What did our delegates have to say about this course?

Took the intermediate course at the beginning of lockdown and I can honestly say I am using R every day now (and having so much fun!)-recommend to anyone who has data to analyse. Dr Helen Wright, Career Development Fellow Versus Arthritis; Tenure Track Fellow, University of Liverpool.

Can’t recommend these courses and the support team enough! Fab accessible content that got this (former) R-phobic coding and growing in confidence. Dr Hannah Davies, Postdoctoral Research Associate, University of Liverpool.

Thanks to the fantastic @LivUniCBF 'R for data science' course... wish I'd done this years ago! Dr Katharine Stott, Wellcome Trust Clinical Fellow.

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