CANCELLED! Introduction to scientific computing


Course presentation


The modern world is ever more dependent on computer science and information technology advances. Especially in life science in recent years, exponentially growing amounts of biological information have been obtained and deposited in various databases. The predominant source – but not only – of this data is “high-throughput” experimentation, involving simultaneous execution of hundreds or thousands of experiments. A comprehensive understanding of biological phenomena can be achieved only through the integration of all available biological information and different data analysis tools and applications. It becomes crucial for modern scientists to acquire skills that will enable them to efficiently deal with this “data explosion”. It is not surprising that scientists with strong computer skills fare better in their position and research. This course aims to provide learners with the basic notions underlying computer science as necessary for biomedical science. The focus of this course in on computer architecture, scientific computing and basic programming.

Aim of the course

To provide participants with basic knowledge on computer architecture, available storage and computing resources, good IT practices, and some programming skills in Python.

By the end of the course, the participants should:

  • understand personal computer & cluster architecture along with mass-storage, and their usage;
  • be acquainted with the available IT resources and people in charge at the GIGA/ULiège IT infrastructure;
  • be aware of some principles of good programming practice;
  • know about the main operational systems & programming languages;
  • write basic scripts and functions in Python.

Python programming

This module of 5 x 3h trainings will teach you the basics of Python scripting. At the end of the formation you will be able to write simple scripts and be familiar with the Python syntax, data types, flow control, file input/output and error catching. This won't make you a Python expert in the blink of an eye but rather give you the tools to start your Pythonista journey and be able to grow independently.

No particular pre-requisite is required. Although it is quite possible to follow the courses in a passive way, it is much better to bring your own laptop to interact and practice live.

The material for the Python course can be found here. This introduction will cover the 5 basics courses.


Target group

PhD candidate, postdoctoral researchers and PI’s. 
Course is limited to 14 participants.

Duration of the course and location

5 days split between the GIGA+5 and CRC (B30):

  • November 21st, B34/+5, GIGA - Auditoire Léon Fredericq
  • November 28th, B30, CRC - Big meeting room
  • December 5th, B34/+5, GIGA - Grande Ghuysen
  • December 12th, B34/+5, GIGA - Auditoire Léon Fredericq
  • December 19th, B34/+5, GIGA - Grande Ghuysen

In total, 30 hours.


For the practical and programming sessions:

For GIGA members, the bioinformatics team wiki provides practical information about the mass-storage and cluster, including good practice recommendation and how to connect. Do not hesitate to contact them if you don't have access or are not sure you have access to the GIGA mass storage.

IT requirements: at the very least make sure you have your 'u' or 's' account (preferably the former)

Organization of the course

The course is a mixture of in-person theoretical lectures, live demonstrations and practical sessions:

  • 5 days, over the course of 5 weeks, for in-person teaching with
    • in the morning, lectures, demo and hands-on;
    • in the afternoon, python basics.
  • the remaining 3 weeks, with on line programming courses and exercises to solve, plus extra-lectures on topics covered in-person (when available).

Some of the lectures were recorded in October 2020 and are available here.

Speakers & Contributors

in alphabetical order:

Support resources

Additional resources & extra lectures

Python online courses:

Scientific computing, IT & Linux:

Reproducibility & reliability, QA & QC in software, and Open Source Software

Git, GitHub, Gitlab