Current course offerings

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Course description

This course will provide students with linear algebra and multi-variable calculus foundations used in advanced statistics methods, data modelling, and the analysis of large data sets. In particular the course introduces mathematical basis for data science techniques and objects such as clustering, least squares data fitting, covariance matrices and joint probabilities, multi-variate Gaussian distributions, and principal component analysis (PCA). This introduction follows the path of linear algebra mixed with aspects of multi-variable calculus. In particular , students will be introduced to: Gaussian Elimination, Determinants, Orthogonal matrices, Eigenvalues, Eigenvectors, Diagonalization, Least Squares, and Singular Value Decomposition.

Archived offerings