Three hours lecture per week. This course is a continuation of 260. Topics covered include gravitation, electricity, basic circuits, magnetism, and optics. Offered spring semester. Must be taken concurrently with 271. MATH 221 taken concurrently is also recommended. Prerequisites: grade of C or higher in 260; grade of C or higher in MATH 220.

Two hours lab per week. Lab component for 250 and 260. Offered fall semester. Must be taken concurrently with 250 or 260. NS

An introduction to the analysis of the real number system. Topics include continuity, differential calculus, integral calculus, sequences and series. Prerequisite: grade of C or higher in 221. QL

Topics in Euclidean and other geometries; foundations of geometry; place of Euclidean geometry among other geometries. Offered every other year. Prerequisite: grade of C or higher in 260. QL

Courses on topics of interest to mathematics students offered on the basis of need, interest, or timeliness. Prerequisites: as determined by the instructor. Restricted to students with juniorstanding or higher. May be repeated for credit. For specific section description, click to the Section Details in VitNet.

Students will participate in an off campus applied mathematics internship. Internship placements may be with or without pay, and must be established prior to enrollment in this course in consultation with career services office and/or a mathematics faculty member. May be repeated for credit. Restricted to students with junior or senior standing. Graded CR/NC.

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)

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.