Python introduction light course
Python introduction light course
26 May 2026, 9h00- 12h30
02 June 2026, 9h00- 12h30
General context
This training is designed for researchers and support staff who are new to Python and need a gentle introduction before attending more advanced courses, such as Python for spatial data analysis. It addresses the common challenge of navigating Python-based workflows without prior coding experience.
You will gain practical familiarity with Python environments, syntax, and basic operations, enabling you to understand and adapt existing scripts used in research contexts. While it won't teach you to write Python code from scratch, it will equip you with the foundational knowledge needed to follow and modify scripts in future training events. For those seeking to develop full scripting skills, we recommend the "Programming in Python: master your own project" course.
- Identify basic Python data types such as lists, tuples, and dictionaries.
- Use Jupyter notebooks to run and modify existing Python code cells.
- Set up a Python environment and install packages relevant to data analysis workflows.
- Explain the purpose and structure of Python functions, including arguments and return values.
- Apply conditional statements to control the flow of simple Python scripts.
- Modify existing Python scripts to adapt them to specific research needs.
This online training introduces participants to the basics of Python in a research context. Through guided exercises and demonstrations, participants are expected to follow along in Jupyter notebooks, explore Python syntax, and practice modifying simple scripts.
If you are intending to follow the Spatial Omics Summer School and you have no experience with Python, this course is a prerequisite.
Note that in order to follow the Spatial Omics Summer School you also have to follow Python for spatial data analysis to get acquainted with Pandas, NumPy, and Matplotlib.
No prior coding experience is required. Active participation during the sessions and completion of short hands-on tasks will help reinforce key concepts and prepare you for more advanced Python-based courses.
Trainers
Janick Mathys
Janick tries to help VIB scientists analyze their data by offering bioinformatics training and support. Next to organizing trainings, creating e-learning courses and teaching statistics, R, Python, Linux, HPC, bulk and single cell RNA-Seq analysis, she consults scientists and develops pipelines for omics analyses. Before joining VIB, she worked as a post-doc at KULeuven, doing research on transcriptomics and transcription regulation. She coordinated the Master of Bioinformatics program of KULeuven and taught the course on Biological Databases.