3 credit(s) This course introduces mathematical background needed for the core data analysis methods of dimension reduction, data visualization, classification and clustering. This is not primarily a course in statistics, as it approaches data problems via linear algebraic methods such as Principal Component Analysis (visualization and dimension reduction), Fisher Linear Discriminant Analysis (classification), and K-means (clustering). Case studies will provide an immersive student experience analyzing high-dimensional data problems.