Independent reading and/or research under the guidance of a mathematics faculty member. Refer to the academic policy section for independent study policy. Independent study contract is required. May be repeated for credit.
Work with an interdisciplinary team to solve a realistic and complex problem. Teams will provide status reports and proper documentation throughout the project. Restricted to students with senior standing. (Equal to ENGR-498)
A continuation of Analytics Capstone 1. Work with an interdisciplinary team to solve a realistic and complex problem. Teams will provide status reports and proper documentation throughout the project. Prerequisite: grade of C or higher in 498. (Equal to ENGR-499)
Three hours lecture/two hours lab per week. An introduction to astronomy, earth science, chemistry, and physics. Students will learn about current events that relate to these topics and how to think critically about scientific information as an informed citizen. NS
Three hours lecture per week. An introduction to the fundamental principles of physics using algebra and trigonometry designed primarily for biology and pre-health students. Topics covered include kinematics, dynamics, oscillatory motion and fluid mechanics. Offered fall semesters. Must be taken concurrently with 270. Prerequisite: placement into MATH-220 or concurrent enrollment into MATH-113. NS
Independent reading and/or research under the guidance of a mathematics faculty member. Refer to the academic policy section for independent study policy. Independent study contract is required. May be repeated for credit.
Multivariate calculus: three-dimensional coordinate system, vectors functions, partial differentiation, multiple integration, integration in vector fields, and applications. Prerequisite: C or higher in 221. QL
Four hours lecture per week. First order equations, second order linear equations, linear systems of equations, numerical methods, nonlinear systems and phase place analysis, matrices and linear systems, matrix operations, determinants, linear transformations, vector spaces, eigenvalues and eigenvectors. Prerequisite: grade of C or higher in 221. QL
Random variables, probability theory, application, and simulation. The binomial, Poisson, geometric, normal, gamma, and chi-square distributions are studied. Additional topics covered as determined by the instructor. Prerequisite: grade of C or higher in 220; grade of C or higher in 130 or 230. QL
Data analysis using simulation, machine learning algorithms: logistic regression, Naive Bayes, decision trees, k-means, k-nearest neighbors, and dimension reduction (principal component analysis). Students will learn how to test and validate models as well as format and display data. A data analysis project will be completed. Prerequisite: grade of C or higher in 230.