About the course | Intended audience | Prerequisites | Content details
About the course
A practical introduction to data analysis and visualisation using the Python programming language.
Python is one of the leading programming languages in data science and is widely used for data analysis, visualisation, statistics and machine learning. This course introduces the core skills needed to work effectively with scientific datasets in Python, with an emphasis on exploratory data analysis, data manipulation and visualisation.
The course is entirely based on open-source software, and all tools used during the training are freely available.
By the end of the course, participants should be able to:
- work confidently with Python and interactive coding environments
- import, inspect and perform quality control checks on tabular datasets
- manipulate and reshape datasets for downstream analysis
- summarise and analyse grouped data
- create and customise data visualisations suitable for scientific communication
- combine datasets using shared identifiers and resolve common data-cleaning issues
Teaching is primarily hands-on, combining short presentations with live coding demonstrations and guided practical exercises.
Back to top Intended audience
This course is suitable for:
- researchers and students who are new to data analysis in Python
- participants interested in practical data science skills for research applications
- researchers working with biological, biomedical or experimental datasets
- participants with some prior programming experience who want to strengthen their Python data analysis skills
Prerequisites
No prior programming experience is required.
The course is designed for complete beginners, although participants with some previous coding experience may also benefit from the practical exercises and refresher material.
Content details
The course combines lectures, demonstrations and hands-on exercises delivered through slides, live coding sessions and dedicated online training materials.
Topics covered include:
- Working with tabular data in Python
Introduction to data analysis workflows using Python data science libraries. Participants learn how to load, inspect and explore tabular datasets for biological and experimental research applications.
- Data manipulation and transformation
Using Python tools to filter, sort, reshape and summarise datasets. The session focuses on practical techniques for preparing data for downstream analysis and visualisation.
- Exploratory data analysis
Performing descriptive analyses and investigating patterns, distributions and relationships within datasets. Participants learn how to identify trends and detect common data quality issues.
- Combining and cleaning datasets
Working with multiple datasets using shared identifiers, handling missing values and resolving common formatting and encoding issues encountered in real-world datasets.
- Data visualisation in Python
Creating informative visualisations using Python plotting libraries, including customising plot appearance and producing clear, publication-ready figures.
- Reproducible analysis workflows
Introduction to practical coding workflows for organising analyses, structuring scripts and working reproducibly with computational data analysis projects.