I took this course as a freshman in my chemical engineering program. This course is similar to this Udacity course, but with a bit of extra detail and minus the Python. Officially called "Introduction to Linear Algebra", the course description is as follows:
- Vectors, dot and cross products, lines and planes, projections.
- Vectors in n-space.
- Systems of linear equations.
- Matrix algebra and linear transformations, inverses.
- Spaces and subspaces.
- Linear independence, basis and coordinates, dimension, rank.
- Determinants, Cramer's Rule.
- Eigenvectors, eigenvalues and diagonalization with applications.
- Orthonormal bases and symmetric matrices.
How Is It Useful?
Math is one of the key building blocks of data science. Linear algebra is essential for more advanced topics in data science such as machine learning, algorithms, and advanced statistics.1 Check out this Quora page for a great summary of the use cases of linear algebra in data science.