3 Credit Hours. The course covers representations of data for efficient manipulation and visualization. These include, Dimensionality Reduction, Clustering, Euclidean Embedding, Graph Embedding, and Discriminant Functions, principal component analysis (PCA), singular value decomposition (SVD), and randomized techniques. The course gives an introduction to numerical optimization methods. Methods include constrained convex optimization problems, method of Lagrange multipliers, and others. This project-based course is designed to enhance students' practical skills in Data Engineering and Analysis.