Advanced Research Computing



We offer courses that are freely available for all researchers at UCL, from postgraduate research students to senior research staff related to research including high-performance computing, research.

Becoming a digital researcher

Software Carpentry

What computing skills do you need to produce reproducible research?

  • Instructor-lead
  • 2 days
  • 5 times per year
  • Delivery: Online & in-person
  • Provider: ARC
Introduction to the Unix Shell

How can we automate tedious repetitive tasks?

Open Science and the future of research applications
DSD: In a Nutshell: Git version control



Improving research software

Introduction to programming with Python for Research
Introduction to research software development with Python
DSD: An Introduction to R with Rstudio
Tips and techniques for developing research software, or how not to be slated by the media

Managing your research data

Storing and sharing your research data
Information Governance, sensitive data, and the Data Safe Haven

High-performance computing

From laptop to supercomputer: HPC Carpentry for UCL clusters

What to do when your computer is not powerful enough?

  • Instructor lead
  • Duration: 2 days (12 hrs), 4 sessions
  • Frequency: 3 times per year
  • Delivery: Online or in-person
  • Provider: ARC
  • Access: View ARC events calendar for upcoming dates
Efficient and secure use of the UCL compute clusters
Python in High-Performance Computing

Learn how to analyse Python programmes and identify performance barriers to help you work more efficiently.

Managing big data with R and Hadoop

An introduction to the MapReduce paradigm for distributed data processing on a cluster. Some experience with R, statistics and matrix operations is recommended.


An introduction to the theory and practice of parallel computing. Provides a good explanation of different computing architectures and the pros and cons of each.

    Introduction to HPC - ARCHER

    A collection of YouTube videos, slides and exercises from ARCHER’s introductory high performance computing course. The course explains the theory and practice of parallel computing with a nice variety of practical examples.

    • Provider: Self-paced learning
    • Provider: ARCHER, EPCC
    • Details and access: Intro to HPC



    Data analysis and data science

    Machine Learning

    Excellent course on the basic but still powerful and relevant methods in machine learning, easy to follow. The course is at an intermediate level, and Andrew Ng has a great way of explaining complicated concepts in a simplified and practical way.

    Deep Learning

    A follow-up on the Machine Learning course above, with a focus on Deep Learning, presented in the context of the main applications such as Computer Vision and NLP. Highly recommended.

    • Self-paced learning
    • Provider: Coursera
    • Details and access: Deep Learning