Biostatistics, M.S.

I. Contact Information

So Young Ryu, Ph.D., Assistant Professor
(775) 784-1440
soyoungr@unr.edu

Wei Yang, Ph.D., M.D., Professor
(775) 682-7094
weiyang@unr.edu

II. Brief Description

The M.S. program in Biostatistics will support the vision of the University of Nevada, Reno. This program will educate the best-prepared leaders who have rigorous biostatistics training in the emerging research areas (i.e. bioinformatics, genetics, big data in medicine). In addition, this program will train students to solve the vital issues of our time - challenges of big data analysis in public health and biomedicine. The Biostatistics Master of Science is a popular degree as either a terminal degree or together with a doctorate degree in biology, computer science, or math. The demand for biostatisticians is steadily rising in public health organizations, government agencies, biotechnology companies, and pharmaceutical companies. The projected employment growth rate for statisticians in the United States from 2016 to 2026 is 33%. Research funding agencies such as National Institutes of Health (NIH) and National Science Foundation (NSF) also recognize the important role of statisticians in biomedical research requiring rigorous experimental designs and sound statistical analysis plans in grant proposals.

This program will make it possible to educate future leaders in this emerging area. The projected enrollment in this program is 30 full-time students. This program requires a minimum of 36 graduate credits with either thesis or professional project options.

III. Student Learning Outcomes

Upon completion of the Biostatistics, M.S. program, students will have:

• A comprehensive understanding of the basic concepts of modern statistical theory and their probabilistic foundation.
• The computational skills necessary to conduct statistical analysis using appropriate statistical methods and software.
• The ability to critically evaluate new and existing statistical methodology.
• The breadth of knowledge and communication skills necessary to become an effective collaborator and/or consultant in modern interdisciplinary research.
• Professional-level preparation in writing reports and interpreting inferential findings based on the statistical analysis results.

IV. Admission Requirements

• GRE Quantitative 60 percentile and above,
• Undergraduate GPA 3.0 and above,
• Calculus I & II, and
• Bachelor’s degree (e.g. B.A., B.S.) from an accredited institution.

V. Program Requirements (36 units)

VI. Recommended Schedule