Why study Biostatistics?
The Master of Science in Biostatistics program provides an innovative curriculum to meet a range of professional needs and interests. The program trains students in study design, appropriate use of statistical and computational techniques, and interpretation of data analysis results arising from public health and biomedical research. Students learn both classical and modern statistical methods and apply their statistical skills and knowledge to solve “big data” problems. They have the opportunity to work with faculty on cutting edge research projects, such as discovering genetic causes of common diseases, developing statistical algorithms to monitor treatment efficacy, and reducing medical costs through electronic health record.
M.S in Biostatistics students complete 33 credits of required courses, and 9 elective credits, for a total of 42 credits.
Required Courses (33 credits)
PH 702 Introduction to Biostatistics, 3 cr
PH 704 Principles & Methods of Epidemiology, 3 cr
PH 711 Intermediate Biostatistics, 3 cr
PH 712 Probability and Statistical Inference, 3 cr
PH 715 Applied Categorical Data Analysis, 3 cr
PH 716 Applied Survival Analysis, 3 cr
PH 717 Applied Longitudinal Analysis, 3 cr
PH 718 Data Management, Visualization, and Advanced Statistical Computing, 3 cr
PH 801 Seminar in Public Health Research, 3 cr
PH 813 Practice of Biostatistical Consulting, 3 cr
PH 895 Research and Thesis for MS in Biostatistics, 3 cr
Required Subject Matter “S”electives (Choose two courses, 6 cr.)
PH 714 Statistical Genetics and Genetic Epidemiology, 3 cr
PH 721 Intro. To Translational Bioinformatics, 3 cr
PH 722 An Introduction to Bayesian Statistics, 3 cr
PH 723 Clinical Trials (3) PH812 Statistical Learning and Data Mining, 3 cr
PH 812 Statistical Learning & Data Mining, 3 cr
PH 818 Statistical Computing, 3 cr
ED PSY 823 Structural Equation Modelling, 3 cr
ED PSY 832 Theory of Hierarchical Linear Modeling, 3 cr
CS 708 Scientific Computing, 3 cr
CS 711 Pattern Recognition – Statistical, Neural, and Fuzzy Approaches, 3 cr
Elective (Choose 1 course, 3 cr.; other courses as approved)
PH 703 Environmental Health Sciences, 3 cr
PH 705 Public Health Policy and Administration, 3 cr
PH 706 Perspectives in Community and Behavioral Health, 3 cr
BIOL 597 RNA Structure, Function, and Metabolism, 3 cr
BIOL 490 Molecular Genetics, 3 cr
Visit the Academic Catalog for the most up-to-date information.
MS Biostatistics Competencies
1. Perform all responsibilities of a statistician in collaborative research; in particular: design studies, manage and analyze data and interpret findings from a variety of biomedical, clinical or public health experimental and observational studies.
2. Communicate statistical information effectively with individuals with varying degrees of statistical knowledge through written and oral presentations.
3. Use statistical, bioinformatic and other computing software to organize, analyze, and visualize data.
4. Review and critique statistical methods and interpretation of results in published research studies, presentations, or reports.
5. Understand and implement modern statistical approaches emerging in the literature to improve biomedical and public health.
Graduates will be prepared for many career paths, including academia, managed care organizations, the pharmaceutical industry, and public service at all levels of local, national and international government.
Faculty expertise in: Genetic determinants of common chronic diseases (including heart disease, bleeding disorders, Type II diabetes, stroke and colorectal cancer); using genomic technologies and bioinformatic and biostatistical techniques to accurately predict risk and treatment response in cancer and cardiovascular disease; major molecular mechanisms and pathways that modulate disease progression; using biomedical informatics, mathematical modeling and simulations to characterize and predict the use of genetics in medical practice and in particular pathology; use of high-throughput genetic technologies such as micro-arrays and next generation sequencers in the discovery and applications of genetics to complex diseases and environmental-gene development pathways; and statistical methods and computational tools to identify genetic variants that influence the susceptibility to complex diseases such as cancer of the breast, colon/rectum, lung and prostate.