Computational Analysis (Undergraduate Level)

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

🔰 Audience & Prereqs

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

🎯 Learning Outcomes

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).

🗂 Repository Layout

.
├─ 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

🚀 Quickstart (Codespaces)

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

📦 Local Install

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

🌐 Build & Publish the Course Website

• 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.

🧑‍🏫 Instructor Notes

• 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.

🔗 License

MIT — see LICENSE.