This repository contains the material for the course Python Programming I, aimed at biomedical and life science researchers who want to learn practical Python for data analysis and bioinformatics.

The full rendered tutorial is available here:

👉 https://sccbioinformatics.github.io/Python_programming_1/

This course introduces:

• Basic Python syntax and data structures
• Numerical computing with NumPy
• Data handling with pandas
• Plotting with matplotlib and seaborn
• Flow control and functions
• Performance considerations (vectorisation vs loops)
• Clustering and heatmaps for gene expression data
• Linear models and ANOVA
• A programming project (k-means clustering)
• A final project on simulated annealing

The focus is on:

• learning how to think like a programmer
• writing readable and correct code
• understanding what common bioinformatics tools do internally

The tutorial is automatically built and deployed using GitHub Actions:

https://sccbioinformatics.github.io/Python_programming_1/

You do not need to build it locally unless you want to modify the content.

.
├── docs/                 # Tutorial source files (Markdown)
│   ├── index.md
│   ├── 01-setup.md
│   ├── 02-basics.md
│   ├── ...
│   └── 16-Simulated_annealing_final_project_introduction.md
├── mkdocs.yml            # MkDocs configuration
├── requirements.txt      # Python dependencies for building the site
└── .github/workflows/    # GitHub Actions workflow

If you want to build the website locally:

pip install -r requirements.txt
mkdocs serve

Then open:

http://127.0.0.1:8000

This course is designed for:

• biomedical laboratory scientists
• life science students
• researchers entering bioinformatics
• anyone with basic programming exposure (R helpful but not required)

No prior Python experience is assumed.

This material is intended for teaching and academic use. You may reuse and adapt it with attribution.

Maintained by the SCC Bioinformatics group.

If you find errors or have suggestions, please open an issue or pull request.