Essential Linear Algebra for Data Science

This Colorado Boulder course selects the linear-algebra ideas most directly useful in data science. It moves from Gaussian elimination and matrix arithmetic to determinants, eigenvectors, projections and least squares. The narrower scope is helpful when you want a mathematical refresh without committing to a full proof-based university sequence.

What you will learn

• Analyze a system through Gaussian elimination.
• Relate eigenvectors to matrix structure.
• Form a projection or least-squares problem.

Who this suits

The provider recommends beginning algebra and basic graphing, while labeling the course intermediate. Use the opening material to judge your preparation.

Before you enroll

A compact applied course necessarily selects its topics. Follow it with additional practice or a fuller vector-space treatment if later study requires proofs and abstraction.

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

Take a small overdetermined system and explain why an exact solution fails. Find an approximate solution and describe what its residual says about the original observations.

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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