Students applying must have good quantitative skills with an undergraduate or higher degree from an accredited institution in mathematics, statistics, biostatistics, computer sciences, biomedical engineering, or other closely related areas. All prospective students must provide evidence of basic skills in computer programming through coursework. Typically, we expect all applicants to have taken the following courses or their equivalents. It is recommended that candidates will also have taken basic coursework in human biology and/or genetics. If you have any questions about whether a course will meet our prerequisites, please inquire at firstname.lastname@example.org or by calling 314-362-1384.
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.
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 basic course in 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.
HIGHLY RECOMMENDED COURSES:
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.
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.
Acceptance of Transfer Credits
We will accept transferable credits from other accredited institutions of higher education and from other programs at Washington University, provided that the didactic courses are considered to be essentially equivalent to the courses offered in our program. The Program administration will make such determinations. However, in order to graduate from the MSBDS program, at least 75% of the credits must be completed through the MSBDS program (residency requirement). Therefore, MSBDS will accept a maximum of 10 transfer credits.