# weijie-chen/Linear-Algebra-With-Python

Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.

Repository: https://github.com/weijie-chen/Linear-Algebra-With-Python
Canonical: https://ross.abutalabs.com/products/linear-algebra-with-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: mathematics, linear-algebra, python, jupyter, data-science, data-visualization, data-analysis, matrix, matrix-calculations, computational-science, eigenvalues, eigenvectors, singular-value-decomposition, multivariate-normal-distribution, diagonalization, linear-transformations, null-space, gram-schmidt, symmetric-matrices, vector-space
Last push: 2024-09-05T16:40:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2284, "days_push": 727, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2569, forks 617 (observed 2026-08-28T04:07:01.317950+00:00)

## What it is
A collection of Jupyter Notebook lecture notes teaching linear algebra concepts with Python computation and visualization using NumPy, SymPy, and Matplotlib. It targets students and practitioners such as data scientists, quantitative analysts, and econometricians who want to refresh linear algebra foundations for data science and quantitative fields.

## Use cases
- learn linear algebra with python
- refresh matrix algebra for data science
- understand eigenvalues and eigenvectors with code examples
- visualize linear transformations and vector spaces
- study SVD and diagonalization with jupyter notebooks
- prepare for econometrics or machine learning math foundations

## When to choose
- you want intuition and visualization rather than rigorous proofs
- you know basic Python, NumPy, and Matplotlib and want applied linear algebra
- you need a free refresher before studying data science, statistics, or quant finance

## When to avoid
- you need a rigorous proof-based linear algebra course
- you want production-quality or efficient numerical code
- you need a structured course with graded exercises and certification

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-visualization, math
- domain: mathematics, data-science, tutorials, education
- platform: python, cross-platform
- tags: linear-algebra, jupyter-notebooks, lecture-notes, numpy, sympy, matplotlib, matrix-computation, eigenvalues, svd, self-study, education

## Member repositories
- weijie-chen/Linear-Algebra-With-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:01.317950+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:20.934482+00:00, confidence not recorded.
  - readme: https://github.com/weijie-chen/Linear-Algebra-With-Python (fetched 2026-08-28T04:07:01.317950+00:00, sha 0016dadca532)
- Data as of 2026-08-30T08:39:29.467469+00:00.