Self-Paced College-Level Linear Algebra Course

Linear Algebra

Self-Paced College-Level Linear Algebra Course

Linear Algebra is an essential skillset required in Data Science, engineering, and many other areas. In this self-paced, college-level course students learn by doing, completing hands-on interactive coursework with remote support of dedicated NCLab instructors.

Recommended Background

To be successful in this course, you should be comfortable with basic high school math. Simple calculus (polynomial integration) is involved in some applications including best polynomial approximation. No prior knowledge of Python or computer programming is required.

Student Learning Outcomes (SLO)

Students will be able to:

  • Perform all standard vector and matrix operations.
  • Transform linear system to matrix form and solve them.
  • Create echelon and reduced echelon forms of matrices.
  • Determine basic and free variables in linear systems.
  • Express infinite solution sets in parametric vector form.
  • Work with determinants and elementary matrices.
  • Create and use LU and Cholesky matrix factorizations.
  • Work with linear transformations associated with matrices.
  • Calculate eigenvalues and eigenvectors.
  • Work with linear spaces, bases, and coordinates.
  • Perform orthogonal projections and decompositions.
  • Diagonalize matrices.
  • Perform spectral decomposition of matrices.
  • Solve Least-Squares problems via normal equations and QR factorization.
  • Perform Singular Value Decomposition (SVD) of matrices.
  • Apply SVD to data cleaning and image compression.

Equipment Requirements

Computer, laptop or tablet with Internet access, email, and one of the following browsers:

  • Google Chrome
  • Mozilla Firefox
  • Microsoft Edge
  • Safari