A complete, self-contained course for undergraduate Computational Analysis using Python. Designed to run seamlessly in GitHub Codespaces, locally, or via Binder.

• Undergraduates in engineering / data science / applied math.
• Comfortable with calculus, linear algebra, and basic Python (variables, loops, functions).

By the end of this course, you will be able to:

• Use Python (NumPy, Pandas) for numerical computation and data analysis.
• Implement and analyze simple numerical methods (root-finding, linear algebra, ODE/PDE basics).
• Build and evaluate basic ML models (linear regression) for empirical modeling.
• Create clear visualizations with Matplotlib.
• Practice reproducible, documented, version-controlled computational workflows (Jupyter Book).

. ├─ book/ # Jupyter Book source │ ├─ _config.yml │ ├─ _toc.yml │ └─ content/ # Markdown pages & notebook references ├─ notebooks/ # Jupyter notebooks (hands-on labs) │ ├─ 01_python_basics.ipynb │ ├─ 02_numpy_linear_algebra.ipynb │ ├─ 03_pandas_data_analysis.ipynb │ ├─ 04_matplotlib_visualization.ipynb │ ├─ 05_optimization_gradient_descent.ipynb │ ├─ 06_cfd_1d_heat_equation.ipynb │ └─ 07_ml_linear_regression_sklearn.ipynb ├─ data/ │ └─ air_quality_sample.csv ├─ courseutils/ # Small helper package used by notebooks │ ├─ __init__.py │ └─ plotting.py ├─ .devcontainer/ │ └─ devcontainer.json # Codespaces config ├─ .github/workflows/ │ └─ deploy-book.yml # Build & publish Jupyter Book to GitHub Pages ├─ .vscode/settings.json ├─ .binder/environment.yml # Binder/Hub environment (optional) ├─ environment.yml # Conda-style env (optional) ├─ requirements.txt # pip deps ├─ syllabus.md # Detailed syllabus & weekly plan ├─ LICENSE └─ Makefile # Quality-of-life commands 

1. Click Code → Create codespace on main.
2. After the container builds, the post-create step installs dependencies.
3. Open any notebook in notebooks/ and run.
4. To build the Jupyter Book site locally:

   make book

python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate pip install -r requirements.txt jupyter lab

• Push to GitHub; the deploy-book workflow builds & deploys the book to gh-pages.
• Enable GitHub Pages in repo settings: Source = Deploy from a branch → Branch = gh-pages.

• The course is modular; assign notebooks as labs or homework.
• Use syllabus.md for a week-by-week plan with outcomes and assessments.
• Keep datasets small in-repo; link to larger data if needed.

MIT — see LICENSE.