For the college’s course catalog, please visit the Courses section. For courses currently offered, please visit the Schedule of Classes.

An introduction to statistical reasoning and methodology. Topics include experimental design, exploratory data analysis, elementary probability, a standard normal-theory approach to estimation and hypothesis testing and linear and multi-variable regression. Not open for credit to students who have taken Psychology 370. (Offered each year)
Variable topics involving: mathematical methods for the Natural and Social Sciences and Mathematical Problem Solving.
A one-semester introduction to single-variable calculus focused on functions, graphs, limits, exponential and logarithmic functions, differentiation, integration, techniques and applications of integration, and optimization. Emphasis is given to applications from the natural and social sciences. (Offered each semester)
This course will explore three major topics of mathematics: linear algebra, probability and statistics, and Markov chains. Using these three topics, students will engage in three real world applications in biology, chemistry, and economics. This course is well suited for students who need a year of mathematics, like many pre-professional programs, and are looking for real applications of mathematics beyond the typical algebra and calculus approach. While this course would be a natural extension for pre-professional students who have take Math 121 Essentials of Calculus, this course only requires a strong background in high school Algebra II.
An accelerated introduction to the calculus of single variable functions with early transcendentals. Topics include limits, derivatives, integrals, and applications of calculus to the natural and social sciences including optimization, defferential equtions, curve, probability, velocity, acceleration area, volume. Net Change theorem, Fundamental Theorem of Calculus. Prerequisite: Placement or Math 121. (Offered each semester)
A continuation of the study of single variable calculus, together with an introduction to linear algebra and the calculus of multivariable functions. Topics include: an introduction to infinite sequences and series, vectors, partial and directional derivatives, gradient, optimization of functions of several variable, integration techniques, double integrals, elementary linear algebra, and an introduction to differential equations with applications to the physical and social sciences. Prerequisite: AP Calculus AB or BC score of 4 or 5 or Math 123. (offered each semester)
A general category used only in the evaluation of transfer credit.
A course used to introduce new intermediate-level courses into the curriculu. (Also listed under Computer Science offerings.)
An introduction to proof writing techniques. Topics will include logic and proofs, set theory, mathematical induction, relations, modular arithmetic, functions, cardinality, number theory, and calculus. Prerequisite: MATH 124 with MATH 231. (Offered each semester)
This course aims to enhance mathematics and computer science students' proficiency and comfort in orally communicating content in their disciplines. Students will develop skills in presenting technical information to a non-technical audience. In particualr, students will deliver a number of presentations during the semester on substantive, well-researched themes appropriate to their status in their major. Prerequisite: Math 210 or CS 271. (Offered each year)
A continued study of Linear Algebra with applications to linear differential equations and mathematical models in the physical and social sciences. Topics include abstract vector spaces over the real and complex numbers, bases and dimension, change of basis, the Rank-Nullity Theorem, linear transformations, the matrix of a linear transformation, eigenvectors and eigenvalues, diagonalization, matrix exponential, linear differential equations of order n, linear systems of first order differential equations, and a continued study of infinite series, power series, and series solutions of linear differential equations. Prerequisite: Math 124. (Offered each semester)
A course in mathematical modeling including linear and nonlinear optimization models, linear and non-linear dynamic models, and probability and statistical models. Both continuos and discrete models are considered. This course focuses on applying mathematics to open ended, real world problems, and effectively communicating conclusions. Sensitivity analysis and model robustness are emphasized throughout. This course also strongly features approximation and simulation methods in conjunction with analytic methods. Prerequisite: MATH 231. (Offered each spring)
This course covers the essentials of asset management including the diversification of investment portfolios. The course begins with the basics of present value analysis and probability theory. Basic tools will be developed and used to study issues such as basic portfolio optimization and asset pricing. Prerequisite: Mathematics 124 (Offered each spring).
Statistics is the science of reasoning from data. This course will introduce the fundamental concepts and methods of statistics, including calculus-based probability. Topics include experimental design, data collection, and the scopes of conclusion, a robust study of probability models and their application to statistical inference, hypothesis testing, and regression analysis. Prerequisite: Math 123. (Offered each fall)
A general category used only in the evaluation of transfer credit.
This course is a capstone experience in oral and written communication for mathematics and computer science majors. Students will research a substantive topic, write a rigorous expository article, and make a presentation to the department. Prerequisite: Math/CS 215. Corequisite: a 300-400 level mathematics or computer science course. (Offered each semester)
A rigorous analysis of limits, continuity, differentiation, integration, uniform convergence, infinite series and basic topology. Prerequisites: Math 210, 231. (Offered every other fall)
A study of general topological spaces, including interiors, closures, boundaries, subspace, product, and quotient topologies, continuous functions, homeomorphisms, metric spaces, connectedness, and compactness together with applications of these concepts. Additional topics may include algebraic topology, including homotopy and homology groups, and/or a parallel study of general measure spaces, including inner and outer measure. Prerequisite: Math 321 or permission of instructor. (Offered every other spring)
An study of Vector Calculus including vector valued functions, curves, Kepler's laws, curvature, torsion, multiple integrals, iterated integrals, Fubini's theorem, polar, cylindrical, spherical coordinates, center of mass, moments of inertia, determinants and n-dimensional volume, change of variables, differential forms, line integrals, Green's Theorem, surface integrals, flux, curl, divergence, Stoke's Theorem, Divergence Theorem, Gauss's law, Maxwell's equations and applications to Topology. The lens is then narrowed to study functions of a complex variable, including an introduction to complex numbers, analytic functions, derivatives, singularities, integrals, Taylor series, Laurent Series, conformal mappings, residue theory, analytic continuation. Cauchy-Riemann Equations, Cauchy's Theorem, Cauchy Integral Formula, Big and Little Picard Theorems, Riemann Mapping Theorem, and Rouche's Theorem. Prerequisite: Math 210, 231. (Offered every other year)
This course is the study of counting techniques for discrete collections of objects. This course will include topics such as permutations and combinations, binomial coefficients, inclusion-exclusion, Fibonacci numbers, Catalan numbers, set partitions, Stirling numbers, generating functions, exponential generating functions, and PĆ³lya counting. Prerequisite: Math 210.
A rigorous analysis of the structure and properties of abstract groups, rings, fields, and vector spaces. Prerequisites: Math 210, 231. (Offered every other fall)
This course is the study of computers as mathematical abstractions in order to understand the limits of computation. In this course, students will learn about topics in computability theory and complexity theory. Topics in computability theory include Turing machines and their variations, the Universal Turing machine, undecidability of the halting problem, reductions, and proving undecidability of other problems. Topics in complexity theory include the classes P and NP, NP-completeness, and other fundamental complexity classes.This course is a study of formal languages and their related automata, Turing machines, unsolvable problems and NP-complete problems. Prerequisites: either CS 109, 110, CS 111, or 112 and Math 210 or CS 234.
This course is about the design and analysis of randomized algorithms, (i.e. algorithms that compute probabilistically). Such algorithms are often robust and fast, though there is a small probability that they return the wrong answer. Examples include Google's PageRank algorithm, load balancing in computer networks, coping with Big Data via random sampling, navigation of unknown terrains by autonomous mobile entities, and matching medical students to residencies. The analysis of such algorithms requires tools from probability theory, which will be introduced as needed. As there have been many randomized algorithms designed to solve problems on graphs, the course introduces numerous topics from graph theory of independent mathematical interest. Graphs are often used to mathematically model phenomena of interest to computer scientists, including the internet, social network graphs, and computer networks. Lastly, this course demonstrates the powerful Probabilistic Method to non-constructively prove the existence of certain prescribed graph structures, how to turn such proofs into randomized algorithms, and how to derandomize such algorithms into deterministic algorithms. Prerequisites: either CS 271 or Math 232, Math 210 and one CS 109, 110, 111 or CS 112.
This course involves mathematical modeling of real-world problems and the development of approaches to find optimal (or nearly optimal) solutions to these problems. Topics may include: modeling, linear programming and the simplex method, the Karush-Kuhn Tucker conditions for optimality, duality, network optimization, and nonlinear programming. Prerequisite: Math 231. (Offered every other fall)
A study of single variable, multi-variable, and stochastic probability models with application to problems in the physical and social sciences. Includes problems in Biology, Finance, and Computer Science. Prereqs: Math 231.
A study of a widely used and applied subfield of advanced Llinear Aalgebra and Calculus(which also uses Calculus). For example, your ear processes a sound wave (maybe from plucking guitar strings) by changing into an orthogonal fre- quency basis allowing us to hear the main notes and some selected overtones. We This course will essentially use the power of changing (orthogonal) bases to analyze a wide array of problems in image processing, sound processing, signal recon- struction, medical imaging, wave analysis, heat diffusion, statistical modeling, quantum mechanics, number theory, and geometry. No knowledge of these application topics is necessary. Prerequisite: Math 231.
A general category used only in the evaluation of transfer credit.
Advanced topics in Abstract Algebra, Analysis, Geometry or Applied Math.
Advanced topics in Abstract Algebra, Analysis, Geometry or Applied Math.