Admission generally requires at least an undergraduate major in a quantitative field (ideally mathematics, statistics, biostatistics, computer science, or biomedical engineering).
Calculus I: Derivatives of algebraic, trigonometric, and transcendental functions, techniques of differentiation and applications of the derivative. The definite integral and Fundamental Theorem of Calculus. Areas. Simpler integration techniques.
Calculus II: Continuation of Calculus I. A brief review of the definite integral and Fundamental Theorem of Calculus. Techniques of integration, applications of the integral, sequences and series, and some material on differential equations.
Elementary Probability and Statistics OR Introduction to Statistics: An elementary introduction to probability and statistics. Discrete and continuous random variables, mean and variance, statistical inference (elementary probability and hypothesis testing), analysis of variance (ANOVA), (multiple) regression, contingency tables. A course covering only probability will not satisfy this prerequisite.
Computer Programming: A basic course in statistical or scientific computer programming. An introduction to software concepts and implementation, emphasizing problem solving through abstraction and decomposition. Introduces processes and algorithms, procedural abstraction, data abstraction, encapsulation and object-oriented programming. Recursion, iteration and simple data structures. Accepted languages include: SAS, R, Matlab, Python, C++. Java and VRB do not meet this requirement.
HIGHLY RECOMMENDED COURSES:
Linear Algebra: An introductory course in linear algebra that focuses on Euclidean n-space, matrices and related computations. Topics include: systems of linear equations, row reduction, matrix operations, determinants, linear independence, dimension, rank, change of basis, diagonalization, eigenvalues, eigenvectors, orthogonality, symmetric matrices, least square approximation, quadratic forms. Introduction to abstract vector spaces.
Data Structures and Algorithms: Study of fundamental algorithms, data structures, and their effective use in a variety of applications. Emphasizes importance of data structure choice and implementation for obtaining the most efficient algorithm for solving a given problem.
Calculus III: Multivariable calculus. Topics include differential and integral calculus of functions of two or three variables: vectors and curves in space, partial derivatives, multiple integrals, line integrals, vector calculus at least through Green’s Theorem.
Introduction to Biology and Genetics: Fundamentals of biology and genetics. A broad overview of genetics, including Mendelian assortment, linkage, chromosomal aberrations, variations in chromosome number, mutation, developmental genetics, quantitative genetics, population genetics, mechanisms of evolution, and phylogenetics.