Develop practical competence in Python as a working tool for research data. This module builds from the language itself to a machine learning workflow, following the same data from a file on disk through cleaning, analysis and visualization to a trained model. Delivered as a hands-on laboratory, it emphasises code that others can read, rerun and trust, connecting everyday programming practice to reproducibility and open data.
Key topics
- Python syntax, data structures, and the scientific library ecosystem
- Numerical and tabular data management with NumPy and pandas
- Importing and exporting data: CSV, JSON, spreadsheets, web APIs
- Data visualization with matplotlib and seaborn
- Machine learning with PyTorch: tensors, autograd, and the training loop
Learning Outcomes
- Write and structure Python code for practical data handling tasks
- Load, clean, transform and visualize research data reproducibly
- Build, train and evaluate a simple neural network model end to end
