Python has become one of the most widely used programming languages in science and data analysis. In many research fields, working with data now means writing at least some code, whether it is processing large datasets, analysing experimental results, automating repetitive tasks, or creating clear visualisations. Python is particularly popular because it is relatively easy to learn while offering a powerful ecosystem of tools for data analysis, statistics, and scientific computing.

In this course, you will learn the core concepts of Python programming through hands-on exercises based on training materials from The Carpentries, an international organisation that teaches foundational coding and data skills to researchers. You will work in Jupyter Notebook, learn how to structure simple programs, and explore commonly used libraries such as Pandas for working with datasets and Matplotlib for visualisation. By the end of the course, you will have the foundations needed to start using Python for your own data analysis and research projects.

High-performance computing (HPC) enables researchers to analyse large datasets and run complex computations that would be impractical on a standard computer. Many modern research fields, from life sciences to engineering and data science, rely on HPC clusters to process data efficiently and run large-scale analyses. Understanding how these systems work and how to use them effectively is therefore an increasingly valuable skill for researchers.

In this course, you will receive a practical introduction to working with an HPC cluster, including how to access the system, transfer files, and run applications using the Slurm scheduler. The workshop is based on training materials from The Carpentries and the HPC Carpentry project, which teach foundational computing and data skills for researchers. Through hands-on exercises, you will learn the core concepts needed to confidently run jobs and workflows on HPC systems.