These courses provide a practical introduction to programming, computing and data science skills for research. They focus on using programming languages and computational tools to solve real-world biological and biomedical research problems.
Recommended starting points
Data Analysis in R
Introduction to the R programming language for data analysis, visualisation and exploratory statistics, with an emphasis on practical data handling and interpretation skills.
Data Analysis in Python
Introduction to data analysis and visualisation in Python, focusing on exploratory analysis, interpretation and practical workflows for scientific datasets.
Fundamentals of Python Programming
Beginner-friendly introduction to Python programming, covering core programming concepts, control structures, functions and practical coding skills for research applications.
Building on from introductory programming
Introduction to the Unix Command Line
Practical introduction to the Linux command line and UNIX environment, including filesystem navigation, text processing and building simple command-line workflows.
Working on HPC Clusters
Introductory overview of high performance computing (HPC), including using shared computing clusters and job scheduling systems for large-scale computational analyses.
Related courses
Reproducible Research
Introduction to reproducible research practices in R, including notebook-based reporting with R Markdown and collaborative version control using Git and GitHub.
Scientific Figure Design
Practical course on designing clear, effective and publication-ready scientific figures for papers, presentations and research communication. Delivered in partnership with the Babraham Bioinformatics Group.
External training opportunities
Programming Courses at the Computing Service
Additional programming and software development courses offered by the University Computing Service.
Unix Training at the Computing Service
Further training opportunities covering Unix, Linux and command-line computing skills.