Overview
This course is an introductory course in data analysis with Python. It provides the student with fundamental skills in data handling using Python. The focus is on exploratory data analysis, leveraging visualizations and data summaries to extract insights from datasets. Additionally, the course covers data cleaning, transformations, and merging data from multiple sources. These competences are developed through the hands-on development of data analysis scripts in an interactive environment. The course emphasizes literate programming, which teaches the student about reproducibility and highlights the importance of well-documented data analysis that can be shared with others.
At the end of the course, the student develops a deeper understanding of virtual environments in Python and the concept of reproducibility. This enhances the reliability of conclusions drawn from data analysis.
Course content
- Visualisation and basic data handling (import/export, cleaning, transforming and summarizing data)
- Reproducible workflows
Prerequisites
- Access to a laptop computer with the ability to install uv, Python and jupyterlab
- No prior knowledge in Python (or statistics) is needed
Schedule
2 November – 18 December 2026
- Video-lectures for code along
- 6 practicals with pair-programming in Python (15.15 – 17.00 on 10/11, 17/11, 25/11, 1/12, 3/12, 9/12, 15/12)
- 2 workshops (12/11 13.15 – 15.00, 19/11 13.15 – 15.00)
- Oral examination of project (17/12 13.15 – 15.00)
Examination
• 1 final written assignment (with peer review + oral examination)
• Active attendance on both workshops
• Active attendance on at least 3 of the 6 practical programming sessions
Teacher
Linda Hartman (Mathematical Statistics)
Registration
Places are limited, early registration is recommended.
Registration will open shortly