Biostatistics

Overview

The Biostatistics at University of California, Berkeley offers training in theory of statistics and biostatistics, the computer implementation of analytic methods, and opportunities to use this knowledge in areas of biological/medical research. The curriculum is taught principally by members of the Division of Biostatistics (School of Public Health) and the Department of Statistics (College of Letters & Science) and provides a wide range of ideas and approaches to the analysis of data.

Learning outcomes

Established in 1955, the Graduate Group in Biostatistics curriculum offers instruction in statistical theory and computing, as well as opportunities to rigorously apply this knowledge in biological and medical research. The degree programs offered (listed below) are appropriate for students who have either a strong mathematical and statistical background with a focus in the biomedical sciences, or degrees in the biological sciences with a focus in mathematics and statistics. (The MA degree can be obtained under Plan I or Plan II. The PhD dissertation is administered according to Plan B.)

The Masters of Arts Degree in Biostatistics is completed in 4 semesters. Candidates for this degree are expected to earn 48 units with courses in biostatistics, statistics, public health, and biology. The 12-unit minimum enrollment requirement per semester may be met with independent research or seminar courses. Students must enroll for all four semesters of the program. Students pursuing the MA degree in Biostatistics will be expected, upon completion of the program, to be well-versed in the following areas:  

• Fundamental statistical methods, including statistical estimation, hypothesis testing, regression analysis, analysis of variance, and longitudinal data. 
• Algorithms and computer applications. 
• Analysis of multivariate data—categorical and continuous—with particular emphasis on epidemiology.
• Interpretation of survival analysis data and issues, including knowledge of life table methodology, competing causes of death, medical follow-up studies, parametric models, and nonparametric methods. 
• Computational biology methods including gene mapping, microarray data analysis, and other topics in genomics.  
• Methods of analysis using vital and health statistics, census data, rates, and adjusted rates.