MPH in Biostatistics

The MPH in Biostatistics (formerly Quantitative Methods) provides the professional training that is common to all MPH Programs of Study in the College of Public Health (the Core MPH requirements) as well as substantive and meaningful training in Biostatistics.  This degree is designed to train public health professionals who can provide leadership in the analysis of public health data and the design of studies for public health investigations. Individuals with an interest in public health and with quantitative ability, but without advanced mathematics training, may find this an interesting career track.

Graduates of the MPH in Biostatistics will be able to:

  • Demonstrate a broad knowledge and understanding of statistical techniques used in public health studies and investigations.
  • Serve as an advocate for good statistical design in public health investigations
  • Apply appropriate statistical methods for inference about public health related questions, and describe the results to public health professionals and educated lay audiences.
  • Interpret the results of statistical analyses in public health related publications for public health professionals and educated lay audiences.
  • Promote the use of sound statistical methods to answer open questions in public health practice.
  • Function as a collaborator on public health projects, taking a leadership role in the design and implementation of projects.
  • Assume responsibility for the design and implementation of analyses in investigations of public health questions.
  • Manage the data for public health related projects such as large community surveys, laboratory investigations, and multi-center clinical trials
  • Demonstrate effective written and oral communication skills when communicating quantitative information and statistical inferences to different audiences of public health professionals

An undergraduate degree is required. No specific major is required. Previous coursework or experience in statistical methods or data analysis is preferred. The cumulative grade point average should be a minimum of a 3.0 on a 4.0 scale. The applicant’s training should include basic course work in computer science and mathematics. The level of training required in each of these areas is described below.

Familiarity with the mathematics of single variable calculus and matrix algebra are required. These requirements can be satisfied by a one semester college course in calculus equivalent to AP Calculus AB and a high school algebra course involving matrices.

Computer Science
Knowledge of elementary computer programming is required. Programming in any commonly used modern programming language (e.g., Python, Java, C++) is acceptable.

Persons with deficiencies in any of the above areas may apply for admission with the understanding that they will gain such experience through self-study in the first semester of enrollment.

MPH Core Courses (18 s.h.)

The following course work is required for all MPH students. Students must earn ≥ B- (2.67) on each core course and must earn a ≥ B (3.0) cumulative grade point average on all core courses. When necessary, a student may repeat courses to achieve this standard.

Number Title Hours
CPH:5100 Intro to Public Health 3 s.h.
BIOS:4120 Intro to Biostatistics OR Biostatistical Methods I 3 s.h.
CBH:4105 Intro to Health Promotion and Disease Prevention 3 s.h.
EPID:4400 Epidemiology I: Principles 3 s.h.
HMP:4000 Intro to the US Healthcare System 3 s.h.
OEH:4240 Global Environmental Health 3 s.h.

Required Biostatistics Courses (10 s.h.)

Number Title Hours
BIOS:5120 Regression Modeling and ANOVA in the Health Sciences 3 s.h.
BIOS:5310 Applied Categorical Data Analysis 3 s.h.
BIOS:5510 Biostatistical Computing (Programming with R) 2 s.h.
BIOS:5510 Biostatistical Computing (Programming with SAS)* 2 s.h.

*This course is waived if previously taken BIOS:5310 Research Data Management

Electives (students select 11-13 s.h. in consultation with advisor)

Electives may be chosen from the following list or may include any related course approved by the student’s advisor.

Number Title Hours
BIOS:6310 Introductory Longitudinal Data Analysis 3 s.h.
BIOS:6610 Statistical Methods in Clinical Trials 3 s.h.
BIOS:6210 Applied Survival & Cohort Data Analysis 3 s.h.
BIOS:7600 Advanced Biostatistics Seminar 1-3 s.h.
BIOS:6810 Bayesian Methods and Design 3 s.h.
Number Title Hours
STAT:3100 Intro to Mathematical Statistics I 3 s.h.
STAT:3101 Intro to Mathematical Statistics II 3 s.h.
STAT:4100 Mathematical Statistics I 3 s.h.
STAT:4101 Mathematical Statistics II 3 s.h.
STAT:5100 Statistical Inference I 3 s.h.
STAT:5101 Statistical Inference II 3 s.h.
Number Title Hours
STAT:4200 Statistical Methods and Computing 3 s.h.
STAT:4520 Bayesian Statistics 3 s.h.
STAT:4540 Statistical Learning 3 s.h.
STAT:6560 Applied Time Series Analysis 3 s.h.
STAT:3210 Experimental Design & Analysis 3 s.h.
STAT:6540 Applied Multivariate Analysis 3 s.h.
Number Title Hours
BIOL:4213 Bioinformatics 3 s.h.
Number Title Hours
IE:4172 Big Data Analysis 3 s.h.

As specified by the Graduate College, a maximum of 6 s.h. may be transferred from another graduate or professional degree.

Applied Practice Experience (2 s.h.), Capstone Experience (1 s.h.), and Interprofessional Experience (0 s.h.)

Number Title Hours
CPH:7500 MPH Applied Practice Experience 2 s.h.
CPH:7200 MPH Capstone Experience 1 s.h.
CPH:5201 Interprof Educ & Pract MPH Students I 0 s.h.
CPH:5202 Interprof Educ & Pract MPH Students II 0 s.h.
CPH:5203 Interprof Educ & Pract MPH Students III 0 s.h.

Pre-Requisites to the Applied Practice Experience and the Capstone Experience

Students must complete all core courses prior to beginning the Applied Practice Experience, and they must take the Capstone Experience course in their final semester.

Summary of Requirements:

Area Hours
MPH Core 18-19 s.h.
Practice Experience 2 s.h.
Capstone Experience 1 s.h.
Interprof Experience 0 s.h.
Required courses 10 s.h.
Electives 11-13 s.h.
Total 42-45 s.h.


* Advanced Biostatistics Sequence Substitution:  A student with sufficient mathematical background can substitute:  Biostatistics Methods I, Biostatistics Methods II, and Biostatistics Methods in Categorical Data (11 s.h.) in place of Introduction to Biostatistics, Regression Modeling and ANOVA in the Health Sciences, and Applied Categorical Data Analysis (9 s.h.).  This advanced sequence has the additional prerequisites of undergraduate multivariable calculus and linear algebra. The advanced sequence requires 2 s.h. fewer elective credits.

Questions or comments? Contact Lexie Just. This page was last reviewed on June 10, 2019.