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Advanced Research Computing

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Training

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.

Training1

Becoming a digital researcher

Computing skills for reproducible research: Software Carpentry
  • What computing skills do you need to produce reproducible research?
  • instructor-lead
  • 2 days / 4 sessions / 12 hours
  • 5 times / 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

Training2

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

Training3

Managing your research data

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

Training4

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
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Efficient and secure use of the UCL compute clusters
  • webinar
Python in High-Performance Computing
  • Learn how to analyse Python programmes and identify performance barriers to help you work more efficiently.
  • Provider: Futurelearn
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.
  • Provider: Futurelearn
Supercomputing

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. Provider: Futurelearn


Training5

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.
  • Provider: Coursera
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.
  • Provider: Coursera