Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors

The second Johns Hopkins course treats matrices as transformations and develops the algebra used to study them. Subspaces and dimension lead into determinants, eigenvalues and diagonalization. This is a broader theoretical progression than a single computational workshop, with attention to how definitions explain familiar calculations and when those calculations are valid.

What you will learn

• Explain matrix multiplication as composition.
• Use subspaces and bases to describe vector sets.
• Determine whether eigenvectors support diagonalization.

Who this suits

The provider uses a mixed-level label, but this course follows its introduction to linear systems. Review vectors, row reduction and linear independence first.

Before you enroll

Memorizing a determinant formula is only part of the work. The course also asks you to connect algebraic conditions with geometric behavior and supporting examples.

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

Compare a diagonalizable matrix with one that lacks enough independent eigenvectors. Explain the practical difference using their actions on vectors rather than only listing eigenvalues.

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.

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