# weijie-chen/linear-algebra-with-python

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2,561 stars · 615 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/weijie-chen/Linear-Algebra-With-Python
- awesome-repositories: https://awesome-repositories.com/repository/weijie-chen-linear-algebra-with-python.md

## Topics

`computational-science` `data-analysis` `data-science` `data-visualization` `diagonalization` `eigenvalues` `eigenvectors` `gram-schmidt` `jupyter` `linear-algebra` `linear-transformations` `mathematics` `matrix` `matrix-calculations` `multivariate-normal-distribution` `null-space` `python` `singular-value-decomposition` `symmetric-matrices` `vector-space`

## Description

Linear-Algebra-With-Python is an educational resource that provides a structured curriculum for learning linear algebra through computational practice. It serves as a tutorial for data scientists and quantitative analysts, bridging the gap between abstract mathematical theory and practical implementation using Python.

The project utilizes a literate programming approach, organizing lecture notes and code examples into interactive documents. By interleaving explanatory text with functional code, it allows users to experiment with mathematical concepts directly within their development environment. This workflow is supported by standard numerical and scientific computing libraries to perform matrix operations, factorizations, and decompositions.

The curriculum emphasizes visual intuition by rendering geometric transformations and vector spaces through integrated plotting tools. These visualizations clarify complex algebraic principles, providing a foundation for applications in data science, financial modeling, and econometrics. The repository is structured as a series of Jupyter Notebooks that facilitate iterative learning and the development of numerical computing workflows.

## Tags

### Education & Learning Resources

- [Data Science Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-science-tutorials.md) — Serves as a comprehensive tutorial for data scientists to master matrix operations and transformations using Python.
- [Linear Algebra Resources](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/academic-curricula-resources/linear-algebra-resources.md) — Provides a collection of lecture notes and code examples for learning linear algebra through computational practice.
- [Jupyter Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula.md) — Provides a structured curriculum for linear algebra delivered through interactive Jupyter notebooks with embedded code and exercises.
- [Literate Programming Notebooks](https://awesome-repositories.com/f/education-learning-resources/literate-programming-notebooks.md) — Uses a literate programming approach to interleave mathematical theory with executable code for reproducible learning.

### Scientific & Mathematical Computing

- [NumPy-Based Linear Algebra Implementations](https://awesome-repositories.com/f/scientific-mathematical-computing/numpy-based-linear-algebra-implementations.md) — Performs matrix operations and linear algebra calculations using optimized NumPy array structures.
- [Linear Algebra Visualizations](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-visualizations.md) — Illustrates abstract linear algebra principles through geometric and visual representations of matrix transformations. ([source](https://github.com/weijie-chen/linear-algebra-with-python#readme))
- [Matrix Factorization Toolkits](https://awesome-repositories.com/f/scientific-mathematical-computing/matrix-factorization-toolkits.md) — Provides procedures for computing matrix decompositions and factorizations to solve complex mathematical problems. ([source](https://github.com/weijie-chen/linear-algebra-with-python#readme))
- [Numerical Computing](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/mathematical-libraries-and-utilities/mathematics/numerical-computing.md) — Facilitates numerical computing workflows by implementing matrix factorizations and transformations for research and data processing.
- [Scientific Computing Curricula](https://awesome-repositories.com/f/scientific-mathematical-computing/scientific-computing-curricula.md) — Offers a structured series of lessons bridging abstract mathematical theory with practical implementation using numerical libraries.
- [Scientific Computing Workflows](https://awesome-repositories.com/f/scientific-mathematical-computing/scientific-computing-workflows.md) — Implements advanced matrix decompositions and factorizations using specialized algorithms from the SciPy ecosystem.

### Part of an Awesome List

- [Data Science Foundations](https://awesome-repositories.com/f/awesome-lists/learning/data-science-foundations.md) — Builds a mathematical foundation for data science by connecting abstract theory to practical Python programming workflows.

### Graphics & Multimedia

- [Matplotlib](https://awesome-repositories.com/f/graphics-multimedia/chart-generators/matplotlib.md) — Renders geometric transformations and vector spaces using the Matplotlib object-oriented API for visual intuition.