MTH - Mathematics Major
Program Title
Program Type
Degree Designation
Department(s)
Program Description
Major effective prior to 2011.
Modifications Made to Curriculum: Fall 2020
Mathematics lies at the heart of the liberal arts. Based in abstraction and logical reasoning, mathematics is both a body of knowledge and an elegant and useful way of perceiving our world. Through mathematics, we can distill and describe the otherwise hidden patterns and relations among things. Because of this, mathematics finds ubiquitous application, from the natural and social sciences to the humanities and the arts. Precise abstraction and quantification play an increasingly important role in these diverse areas, and the study of mathematics can provide a foundation for any of them.
Requisites
Requirements for the Major (48 credits)
I. Core (36 credits)
Complete all of the following:
course - Calculus and Analytic Geometry I OR course - Topics in Single and Multivariable Calculus
course - Calculus and Analytic Geometry II
course - Calculus and Analytic Geometry III
course - Introduction to Statistics
course - Introduction to Computer Science in Python OR course Object Oriented Programming in Java, OR course Introduction to Computer Science in JavaScript OR course Statistical Computing in R
course - Discrete Mathematics
course - Linear Algebra
course - Foundations of Higher Mathematics
course - Probability
II. Proof-Based (4 credits)
Complete 4 credits, selected from the following:
III. Electives: Two electives from the following, with at least one at the upper-level (8 credits)
Complete 8 credits, at least 4 of which are upper level.
course - Introduction to Logic
course - Differential Equations
course - Mathematical Physics
course - Real Analysis (if not taken as the proof-based course)
course - Abstract Algebra (if not taken as the proof-based course)
course - Special Topics in Mathematics
course – Stochastic Processes
course – Optimization and Machine Learning Mathematics
course – Advanced Linear Algebra
course – Complex Variables
course - Modeling and Simulation
course - Symbolic Logic
course - Applied Regression Analysis
course - Statistical Theory (if not taken as the proof-based course)
course - Bayesian Statistics
course - Statistical Machine Learning
course - Computational Modeling of Neural Systems
Other DATA and STAT classes at the 200-level with advisor approval and the submission of a Ladder petition
Notes
For students also majoring in physics, course, course, course, and eight additional intermediate- or upper-level credits may count for both majors.
Students wishing to pursue graduate study in mathematics are urged to take both course and course.