About
This two-day course provides a practical introduction to statistical analysis using Python, combining lectures with hands-on sessions in Jupyter notebooks. Topics include data visualisation and exploration, probability distributions, hypothesis testing, and correlation and regression modelling.
By the end of the course, participants will be able to:
- Load, manipulate, and visualise tabular data using pandas, matplotlib, and seaborn
- Understand and assess the distribution of their data
- Select and apply appropriate statistical tests for common experimental designs
- Interpret test results correctly, including assumptions, p-values, and effect sizes
- Perform and interpret correlation analyses and linear regression models
- Apply these methods independently using Python in a Jupyter notebook environment
Details
Dates: 3.–4. 6. 2026
Location: Institute of Molecular Genetics of the Czech Academy of Sciences, Vídeňská 1083, Praha 4, Jágr’s hall
Maximum capacity: 25 participants
Programme
Day 1 — Data Visualisation & Distributions
- Data manipulation and visualisation
- Distributions and hypothesis testing
- Statistical tests
Day 2 — Statistical Testing & Regression
- Statistical testing, continued
- Correlation and regression
Instructors
The course is taught by Michal Kolář, Jan Kubovčiak, Mathys Delattre, Lucie Pfeiferová, Vojtěch Melichar, and Kateřina Večerková from the Institute of Molecular Genetics of the Czech Academy of Sciences.
Requirements
Basic familiarity with Python is required. Participants are expected to be comfortable with Python syntax and basic data structures. Those who would benefit from a refresher are encouraged to attend the Python Basics course (25.–26. 5. 2026) first.
Participants are encouraged to bring their own laptops. Software requirements will be communicated to all participants upon registration.
Registration
More details coming soon.
