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Courses

A summary of each course to help with your selection.

Academic Year

Programs

Course ID

Course ID	Course
MATH 320	MATH 320 Complex Variables Course Credits: 3 Functions of a complex variable, differentiation, analytic and elementary functions, Cauchy's theorem and contour integration, Taylor and Laurent series, residues and poles, and conformal mapping. Emphasis is placed on physical applications. Prerequisite(s): MATH 223 NB: Not offered every year. See department chair.
MATH 321	MATH 321 Differential Equations Course Credits: 4 First-order differential equations, linear differential equations, Laplace transforms, systems of differential equations, non-linear systems, series solutions, introduction to partial differential equations. Special emphasis is placed on applications to physics and engineering. Cross-listed: PHYS 321 Co-requisite(s): MATH 223, 250 NB: Credit is granted for only one of MATH 311 or MATH 321.
MATH 323	MATH 323 Analysis Course Credits: 4 Sequences and induction; convergence of sequences and series; limits, continuity, and differentiability; Riemann integrals; sequences of functions and an introduction to topology. Prerequisite(s): MATH 150, 223 NB: Credit is granted for only one of MATH 313 or MATH 323.
MATH 327	MATH 327 Numerical Analysis Course Credits: 4 This course covers numerical techniques for solving problems in applied mathematics, including error analysis, roots of equations, interpolation, numerical differentiation and integration, ordinary differential equations, matrix methods and selected topics from among: eigenvalues, approximation theory, non-linear systems, boundary-value problems, numerical solution of partial differential equations. Cross-listed: CMPT 327. Prerequisite(s): MATH 223, 250; CMPT 140. (4-0) NB: Credit is granted for only one of MATH/CMPT 317 and MATH/CMPT 327 and MATH/CMPT 330.
MATH 333	MATH 333 Mathematics of Data Science Course Credits: 3 Foundational mathematical concepts underpinning theoretical frameworks in data science that depend on linear algebra and multivariable calculus, with applications chosen from machine learning, statistical inference, and data assimilation. Possible topics include matrix decompositions, gradient and multivariate chain rule, Lagrange multipliers and constrained optimization, maximum likelihood, and Bayesian estimation. Prerequisite(s): MATH 223, 250 NB: Not offered every year. See department chair.
MATH 340	MATH 340 Discrete Structures & Computing Course Credits: 3 This is a second course in the topics of pure mathematics, particularly those most commonly used in the study of Computing Science and related applications. It includes proof techniques, models of computation, formal languages, analysis of algorithms, trees and advanced general graph theory with applications, finite state and automata theory, encryption, and an elementary introduction to mathematical structures such as groups, rings, and fields. Cross-listed: CMPT 340 Prerequisite(s): CMPT 150 or MATH 150 NB: Not offered every year. See department chair.
MATH 370	MATH 370 Geometry Course Credits: 3 Finite geometries, transformations, Euclidean geometry, constructions, inverse geometry, projective geometry, non-Euclidean geometry. Prerequisite(s): MATH 223, 250 NB: Not offered every year. See department chair.
MATH 381	MATH 381 Topological Data Analysis Course Credits: 3 The development of ideas from topology to manipulate and analyze datasets. Several topics from algebraic topology, geometry, linear algebra, abstract algebra, algorithms, and statistics will be utilized to understand recent results in data analysis. Students will use software for calculations such as persistent homology and Reeb graphs. Applications in fields such as image analysis, sensor networks, clustering, time series analysis, and genetics are discussed. Prerequisite(s): MATH 250
MATH 390	MATH 390 Number Theory Course Credits: 3 This course includes a study of the ideas of classical number theory, their historical development, and modern applications. Topics include divisibility and prime numbers, modular arithmetic, primality tests, primitive roots, quadratic reciprocity, Diophantine equations, continued fractions, and applications such as cryptography. Prerequisite(s): Third-year standing and an understanding of mathematical proof techniques, or instructor's consent

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