Python I

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