(3 crs.) Cross-listed as (AMS), DSP 393G. The course implements an active learning pedagogy for students to meticulously and systematically work with 'Big Data' to develop data-driven predictive models for decision-making. (Lec. 3) Pre: Pre: STA 308 or STA 409 or BAI 210; STA 305 or LTI/DSP 110; and MTH 215. (B3) (D1) (GC)

(3 crs.) Mathematical analysis of Data Science techniques and their implementation in python and R. Monte Carlo methods, Expectation-Maximization (EM) algorithms, Deep Learning, and Functional Analysis with spectral decomposition methods will be covered. (Lec. 3) Pre: MTH 215, MTH 451 and CSC 310 or permission of the instructor.

(1-4 crs.) Topics in applied and computational mathematics. Applications from engineering, biology, finance, data and network science, along with relevant numerical algorithms, will be considered. (Lec. 1-4) Pre: Permission of instructor. May be repeated for a maximum of 12 credits. Not for graduate credit.

(3 crs.) Fundamental concepts of topology, metric spaces, homotopy equivalence. Simplicial complexes. Homology and cohomology groups. Exact sequences. Duality. Persistent homology, persistent diagrams, and their computation. Applications. (Lec. 3) Pre: MTH 215 and MTH 243 or permission of the instructor.

(3 crs.) Cross-listed as (AMS) DSP553. This course covers a wide range of mathematical tools from Discrete Mathematics, Calculus, Linear Algebra, and Probability Theory that arise in Data Science. Each mathematical construct is accompanied by examples of its use in solving practical problems in Data Science. (Accelerated Online Program) Pre: Enrollment in the Online Graduate Certificate in Data Science.

(3 crs.) Cross-listed (AMS), DSP 563. Introduction to mathematical foundations necessary to effectively study problems in data science and machine learning. Use linear algebra and optimization pose and solve modern problems leveraging data from diverse applications. (Lec/Accelerated Online Program) Pre: DSP 556.

(1-4 crs.) Advanced topics of current interest in applied and computational mathematics. Applications from engineering, biology, finance, data and network science, along with relevant numerical algorithms, will be considered. (Lec.) Pre: Permission of instructor.

(1-12 crs.) Number of credits is determined each semester in consultation with the major professor or program committee. (Independent Study) S/U credit.