Program Course Schedule
|Principles of Modern Biostatistics* (4)||Modern Regression Analysis* (4)|
|1||Fundamentals of Probability* (4)||Contemporary Statistical Inference* (4)|
|Introduction to Statistical Computing (2)||Research ethics and scientific writing (1)|
|Elective1 (3)||Elective (3)|
|1st year Comprehensive Examination (end of spring 1st year)|
|Adv. Regression Analysis I (L&GLM)* (4)||Adv. Regression Analysis II (GLM&LDA)* (4)|
|2||Applied Survival Analysis* (4)||Statistical Collaboration in Health Sciences* (4)|
|Elective (3)||Elective2 (3)|
|Ongoing thesis development (no credit)||Ongoing thesis development (no credit)|
|MS thesis presentation (end of spring 2nd year)|
|Total Credits||47||(credits = 32 core + 12 elective + 3 introductory and ethics)|
Notes: * denotes core course for MS; 1 Epidemiologic Theory and Methods is recommended for first-year Biostatistics students; 2 Regression Modeling Strategies (a signature course of this program) is strongly encouraged for students interested in pursuing a PhD or a job in the field of Biostatistics.
The core courses of the MS program are designed to contain the fundamental principles and working knowledge of statistics that MS students will require. The Biostatistics faculty has identified the following as core courses: probability, statistical inference, regression analysis (linear models, general linear models, and longitudinal data analysis), methods for time-to-event data, and collaborative skills. It is expected that electives will be a mix of subject matter courses and topic courses in Biostatistics relevant to the student’s anticipated professional practice. PhD students from departments other than Biostatistics are expected to take longer than the proposed two years to complete the MS curriculum and fulfill all degree requirements.