Curriculum & Concentrations
Campus MS Program

* All courses are 3 credit hours

Core Courses

The following five core courses are required for all M.S. students.

  • PHC 6050c: Biostatistical Methods I
  • PHC 6051: Biostatistical Methods II
  • PHC 6063: Biostatistical Consulting
  • PHC 6937: Introduction to Public Health
  • PHC 6001: Principles of Epidemiology in Public Health

The courses “Biostatistical Methods I and II” make up the methods core of the program. Both courses cover the essentials of statistical methods for different types of data common in health studies.

The core course “Introduction to Public Health” provides a broad introduction to public health as well as an understanding about how public health workers contribute to achieving the goals of public health.

In addition, each student must take the course “Biostatistical Consulting”, which covers communication, management, organization, computational and biostatistical thinking skills necessary to consulting in biostatistics.

The course “Principles of Epidemiology in Public Health” provides students with an overview of epidemiology methods used in research studies that address disease patterns in community and clinic-based populations.

Concentration Core Courses

Biostatistics Methods and Practice Concentration

  • PHC 6092: Introduction to Biostatistical Theory
  • STA 6177: Applied Survival Analysis
  • PHC 6020: Clinical Trial Analysis

The course “Introduction to Biostatistical Theory” provides students with the mathematical foundation necessary to use and understand biostatistical methods.

The course “Applied Survival Analysis” introduces the basic concepts and statistical methods used for analyzing survival data.

Health Data Science Concentration

  • PHC 6099: Programming Basics for Biostatistics
  • PHC 6791: Data Visualization in Health Sciences
  • PHC 6097: Statistical Learning with Applications in Health Science

The core course “Programming Basics for Biostatistics” intends to develop students’ ability to perform statistical computing, and it covers programming topics (e.g., GitHub and building R packages), statistical and computational methods (e.g., optimization), and direct integration and dynamic reporting using R and Python.

In the core course “Data Visualization in Health Sciences”, students will learn the foundations of information visualization, and the course will sharpen their skills in communicating using health science data.

The core course “Statistical Learning with Applications in Health Sciences” covers a broad range of statistical/machine learning methods (e.g., deep learning) that are useful for health data analysis.


Students are also required to complete at least four additional biostatistics/statistics courses determined in conjunction with their supervisory committee. Special topics elective courses will be taught under the course number PHC 6937.

Capstone Experience

During the final semester, students will take a final exam in the form of a written report  to demonstrate mastery of the program. Students must be in good academic standing.

Students will proceed with one of the following two options:

  • Read and critique a paper from the statistical literature, for example, from the journal Statistics in Medicine, and present a summary and critique in a written report form.
  • Complete a data analysis to answer a research question and write a report summarizing the goals of the project, the data source, the methods used, the results of the analysis, and the conclusion.

The students’ academic advisor will serve as the committee chair. There is no length requirement for the written report.

If the first option is selected, the report should consist of a summary of the statistical paper, an application of the statistical methodology to actual or simulated data, and a critique of the strengths and weaknesses of the methodology and the paper.

Comparison to M.S. in Statistics

The curriculum shares some components with the M.S. in Statistics (in particular, the theoretical core because the theoretical underpinnings of statistics and biostatistics are similar and therefore did not require new course development).

However, there is different emphasis in the methodology courses, with the core courses covering methodology for categorical data in Biostatistical Methods II and survival data and clinical trials.

In addition, there is a “subject matter” component in the M.S. in Biostatistics, consisting of the Public Health core courses as well as a consulting requirement.

These are key components in training for Biostatistics, but are not requirements in the M.S. in Statistics.

Learning Outcomes

All graduates of the program will be expected to be able to:

  • Interpret and apply basic biostatistical methods using state-of-the art software in a way that meets the goals of a collaborating health scientist.
  • Support successful collaborations with investigators in new quantitative fields.
  • Interpret biostatistical analyses while remaining aware of limitations.
  • Compete for positions in three primary settings: academic (either in a PhD program or as an academic research assistant), industry, and federal agencies that involve research and/or public health practice.


A minimum of 36 post-baccalaureate credit hours is required. Upon successful completion of the program, graduates will be awarded an M.S. degree in biostatistics. 

PLEASE NOTE: Students who enrolled and began attending traditional M.S. program prior to August 2022 will continue to follow the previous requirements for the program. These requirements can be found in the Departmental Graduate Handbook.

Credit breakdown

Component # of credits
Core Biostatistics courses 15
Concentration Core Courses 9
Biostatistics/statistics electives 12
Total 36