Summer 2022 Training Courses now open

Places are now available for upcoming Research Computing training courses, sign up now!

New upcoming training courses which are now open for registrations!

Whether you want to learn some Git, Linux command line or learn more about the University’s high performance computing systems we’ve got a course for you.

Find booking links on the course web pages below.

June 2022

SWD1b: Introduction to R Programming (On campus)

  • Tuesday 13th June 9.30am till 4pm
  • Monday 14th June 9.30am till 4pm

This course is running as 2 all day sessions delivered in person on campus. Please do not apply for this course unless you are able to attend ALL the sessions. This is an introduction to programming in R for people with little or no previous programming experience. It uses plotting and visualising data as its motivating example based on an established research dataset. The course is suitable for attendees from all research domains and we will use a Web based programming environment (RStudio cloud) which means you will be able to apply the ideas you learn on the course straightaway without having to install any software at home or at work.

At the end of the workshop, attendees will be able to:

  • How to run R programs
  • Storing data in computer programs (using variables and data types)
  • Using built in functions in programs
  • Avoiding and fixing errors in programs
  • Using software other people have written (packages)
  • Reading tabular data and simple statistical analysis
  • Plotting data
  • Storing multiple values using lists
  • Repeating things using LOOPS
  • Creating functions
  • Making programs do different things for different data (Conditionals: IF statements)
  • Writing simple tests: making sure our programs behave properly
  • Programming style

HPC0: Introduction to Linux (Online)

  • Tuesday 21st June 9am-12pm

This is a hands-on workshop intended as an introduction to the Linux command line and shell scripting. It is suitable for Linux, Unix and Mac OSX users. The purpose of the workshop is to give users the Linux skills to be able to handle data and files, run programs and automate workflows on a PC and on the HPC service. The content of this workshop is equally suited for people who wish to use command line Linux on a PC, in the Cloud or on a HPC platform. New and prospective HPC users are advised to take this workshop before taking the HPC1: Introduction to High Performance Computing at Leeds workshop.

HPC1: HPC Carpentry (Online)

  • Tuesday 28th June 9am-12pm

  • Tuesday 5th July 9am-12pm

This workshop is designed to introduce new users to the High Performance Computing (HPC) service at Leeds. It will be useful if you are new to HPC in research or have used HPC elsewhere. It is suitable for researchers from all faculties and examples will be given from a range of research domains. The purpose of the workshop is to give users the skills to be an effective user of the HPC service and to get codes and applications running effectively. This course is split over two sessions a week apart, please do not apply for this course unless you are able to attend ALL the sessions.

SWD6: High Performance Python (Online)

  • Thursday 30th June 9am-12pm

  • Thursday 7th July 9am-12pm

Over the past few years, Python and the wider Python ecosystem have become invaluable tools in scientific computing and data analytics. As Python is (for the most part) an interpreted language there are complaints that Python code can be quite slow to execute. In this hands-on workshop you will have the opportunity discover and use a set of tools and techniques that can be used to improve the execution speed of your Python code. The workshop will introduce a number of ways to both measure the efficiency of your code and improve its speed of execution by introducing strategies for fast and scalable computation with Python.

At the end of this workshop, learners will be able to:

  1. Understand how to profile Python code and identify bottlenecks
  2. Understand how to choose the most appropriate data structure, algorithm, and libraries for a problem
  3. Improve the execution time of Python code using:
  4. Understand when to use each technique

N.B. This course has recently been updated and course contents may change at the time of delivery.