Mathematics

Students must have completed the following courses (or equivalent) with a grade of B (3.0) or better before entering the program:

Elementary Statistics
An introduction to probability and statistics without calculus. Students learn to use statistical reasoning and apply statistical techniques to problems in various health and life science contexts. Topics include probability distribution functions, sampling distributions, estimation, hypothesis testing, and linear regression.

Calculus I
Introduction to the basic concepts of differential and integral calculus and their applications. Limits, continuity, and derivatives; the definite integral and the Fundamental Theorem of Calculus.

Techniques and applications of integration, indeterminant forms, improper integrals, sequences and series.

An introduction to the principles of logic and the methods of proof necessary for the successful study of mathematics. This course serves as a transition from calculus to advanced mathematics courses.

An introduction to the field of scientific computing. Students will use algorithmic reasoning, mathematical software, and programming as tools in mathematical modeling and problem solving.

Systems of linear equations, matrices, vector spaces, linear transformations, inner product spaces, determinants, eigenvectors, and eigenvalues.

Axiomatic and historical development of Euclidean and non-Euclidean geometries. This course will introduce students to the foundations of Neutral and Euclidean Geometry and to some of the subsequent developments in Non-Euclidean Geometries.

An introduction to the theory of sequences, series, differentiation, and Riemann integration of functions of one or more variables.

At least 4 credits must be at the 3000 level.

Calculus III (MATH 2044)
Vectors and parametric equations; functions of two variables; partial and directional derivatives; multiple integrals; line integrals.

Ordinary Differential Equations (MATH 2054)
A first course emphasizing solution techniques of first order differential equations, linear equations of higher order, systems of differential equations, mathematical modeling, numerical methods, existence and uniqueness of solutions and qualitative techniques.

Numerical Analysis (MATH 3034)
An introduction to the field of numerical analysis. Students will learn numerical techniques for solving equations in one variable, interpolation and polynomial approximation, numerical differentiation and integration, and solving initial value problems for ordinary differential equations.

Probability and Statistics (MATH 3044)
An introduction to single variable probability and statistics. Discrete and continuous random variables, conditional probability, expectation, moment generating functions, law of large numbers, central limit theorem, elements of statistical inference, estimation, and hypothesis testing.