Fundamental Linear Algebra Concepts with Python

This Howard University course moves beyond representing data to manipulating matrices and solving equations. Its modules connect inverses and determinants with matrix arithmetic, row reduction and eigenvectors. Python provides a computational setting, while the central decision is whether you want applied calculations or a more proof-focused treatment of vector spaces.

What you will learn

• Use row reduction to solve a linear system.
• Relate determinants to matrix inverses.
• Interpret an eigenvector as a transformation direction.

Who this suits

The provider lists mixed levels. Familiarity with elementary matrix notation and Python will make the computational examples easier to follow.

Before you enroll

Numerical output needs interpretation, particularly when a system has no unique solution. Review the introductory course first if matrix dimensions and vector equations are unfamiliar.

Coursera lists paid enrollment or subscription access. Check the enrollment screen for included assessments, certificate terms, trials and any financial-aid options. University authorship does not by itself confer admission, academic credit or a professional license.

Put it into practice

Choose a two-by-two transformation, calculate its action on several vectors and compare a hand calculation with Python. Identify a direction that the transformation only scales.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.