This is the first workshop in the Math & Coding series, designed for programmers and analysts with basic to intermediate Python skills. We explore key mathematical ideas such as vectors, matrices, determinants, eigenvalues, and eigenvectors — not just in theory, but through a real-world application: Principal Component Analysis (PCA), a widely used method in Data Science and Machine Learning.
PCA serves as a hands-on example to:
- Understand how linear algebra operates inside real code
- Improve our ability to read and interpret PCA outputs
- Connect abstract math to practical machine learning workflows
The goal is not only to learn linear algebra — but to understand the mathematics behind the tools we use in data analysis.
No prior knowledge of advanced math is required. Just curiosity, Python, and a laptop 😉
You have two options to run the notebooks from this workshop:
No installation needed — just click the button below and start working directly in your browser.
You can open and run all notebooks using the Jupyter file browser on the left.
git clone https://github.com/jessica-gonzalez/lin_alg_1.git
cd lin_alg_1pip install numpy matplotlib scikit-learn pandas
If you are using conda:
conda create -n lin_env python=3.10
conda activate lin_env
conda install numpy matplotlib scikit-learn pandasjupyter notebookAll required packages are defined in the environment.yml file, including:
- numpy
- pandas
- matplotlib
- scikit-learn
- torch
- jupyterlab
Feel free to explore, run, and adapt the notebooks to your own data projects. This workshop is meant to build intuition and bridge math concepts with real Python code.