Python essentials for data analysis
Python essentials for data analysis
6 November 2026, 9h00-12h30
9 November 2026, 9h00-12h30
13 November 2026, 9h00-12h30
General context
Turn basic Python knowledge into powerful data analysis skills. This training is designed to bridge the gap between knowing Python syntax and using Python effectively for data analysis in scientific workflows. Throughout the course, you will work with Jupyter Notebooks, manage environments with uv, and explore essential libraries such as Pandas, NumPy, anndata, scanpy, and Matplotlib. These skills will prepare you to understand and adapt Python scripts commonly used in data analysis, giving you the confidence to tailor workflows to your research needs.
- Create software environments including Python packages.
- Work with Jupyter Notebooks to write, run, and document Python code
- Build and manipulate Pandas DataFrames for tabular data analysis
- Generate and handle NumPy arrays for tabular and image data
- Use Matplotlib and Pandas to create visual representations of image data
- Use anndata and scanpy for processing single cell omics data
This training introduces participants to data analysis libraries in Python (numpy, Pandas, matplotlib) in a research context. Through guided exercises and demonstrations, participants are expected to follow along in Jupyter notebooks, explore basic manipulations, and practice modifying simple scripts.
People with no experience in Jupyter Notebooks, Python environments, Pandas, NumPy, anndata, scanpy, or Matplotlib who are planning to follow our imaging with Python, our spatial omics data analysis, or our MOFA2 course.
If you have no experience with Python, consider following our Python introduction course.
If you intend to follow the Integrating multi-omics data using MOFA2 course and have no experience with Python environments, Jupyter, Pandas, NumPy, anndata, scanpy, or Matplotlib, this course is a prerequisite.
This course is part of the learning path Machine learning.
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.