Biostatistics

Many issues in the health, medical, and biological sciences are addressed by collecting and exploring relevant data. The development and application of techniques to better understand such data is the fundamental concern of the Group in Biostatistics. The program 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.

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.)

Master of Arts (MA)

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. Students pursuing the MA degree in Biostatistics will be expected, upon completion of the program, to be well-versed in the following areas:  

• Understand the foundations of statistical inference, e.g., maximum likelihood estimation, regression.

• Have grounding in theoretical framework and ability to apply existing estimators in following categories:

  • Computational statistics

  • Multivariate analysis

  • Categorical data analysis

  • Survival analysis

  • Longitudinal data analysis

  • Causal inference

  • Clinical trials

  • Statistical genomics

  • Statistical computing

• Have fluency in statistical programming languages for both analysis using classic methods and implementation of novel methods.

• Identify and apply sound and pertinent methods to address statistical inference questions in biological, public health, and medical research.

• Effectively communicate research findings, orally and in writing.

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