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.

Examples of careers include:

  • Biostatistician
  • Data Scientist
  • Data Analyst
  • SAS Programmer
  • Informatics Analyst

Prerequisites

  • Although no specific major is required, previous coursework or experience in statistical methods or data analysis is preferred.
  • 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.
  • 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.

A typical student completes the MPH in two years. This following is a sample plan of study based on a full-time student starting in the Fall. Please work with your advisor to choose a plan that works best for you.

Sample Plan of Study for MPH degree in Biostatistics
Course No. Course name Hours
Fall 1 13 s.h.
BIOS:4120 Intro to Biostatistics 3 s.h.
BIOS:5510 Biostatistical Computing (R & SAS) 4 s.h.
CPH:5100 Intro to Public Health 3 s.h.
EPID:4400 Epidemiology I: Principles 3 s.h.
Spring 1 12 s.h.
BIOS:5120 Regression & ANOVA in Health Sciences 3 s.h.
CBH:4105 Intro to Health Promotion and Disease Prevention 3 s.h.
HMP:4000 Intro to the US Healthcare System 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
Fall 2 12 s.h.
BIOS:5130 Applied Categorical Data Analysis 3 s.h.
OEH:4240 Global Environmental Health 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
CPH:5203 Interprofessional Education & Practice for MPH Students 0 s.h.
Spring 2 6 s.h.
BIOS: ### BIOS Elective* 3 s.h.
CPH:7200 MPH Capstone Experience 1 s.h.
CPH:7500 MPH Applied Practice Experience 2 s.h.
MPH degree total 43 s.h.

 

Summary of Requirements
Area Hours
MPH Core 18 s.h.
BIOS Electives 12 s.h.
BIOS Required 10 s.h.
Interprofessional Education & Practice 0 s.h.
Applied Practice Experience 2 s.h.
MPH Capstone 1 s.h.
Total 42 s.h.

 

Sample Plan of Study for MPH degree in Biostatistics: Advanced Sequence
Course No. Course name Hours
Fall 1 14 s.h.
BIOS:5710 Biostatistical Methods I 4 s.h.
BIOS:5510 Biostatistical Computing (R & SAS) 4 s.h.
CPH:5100 Intro to Public Health 3 s.h.
EPID:4400 Epidemiology I: Principles 3 s.h.
Spring 1 13 s.h.
BIOS:5720 Biostatistical Methods II 4 s.h.
BIOS:5730 Biostatistical Methods Categorical Data 3 s.h.
CBH:4105 Intro to Health Promotion and Disease Prevention 3 s.h.
HMP:4000 Intro to the US Healthcare System 3 s.h.
Fall 2 12 s.h.
OEH:4240 Global Environmental Health 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
BIOS: ### BIOS Elective* 3 s.h.
CPH:5203 Interprofessional Education & Practice for MPH Students 0 s.h.
Spring 2 3 s.h.
CPH:7200 MPH Capstone Experience 1 s.h.
CPH:7500 MPH Applied Practice Experience 2 s.h.
MPH degree total 42 s.h.

 

Summary of Requirements: Advanced Sequence
Area Hours
MPH Core 19 s.h.
BIOS Required 11 s.h.
BIOS Electives 9 s.h.
Interprofessional Education & Practice 0 s.h.
Applied Practice Experience 2 s.h.
MPH Capstone 1 s.h.
Total 42 s.h.

 

Suggested Electives
Course No. Course name Hours
BIOS:6310 Introductory Longitudinal Data Analysis 3 s.h.
BIOS:6610 Statistical Methods in Clinical Trials 3 s.h.
BIOS:6210 Applied Survival Analysis 3 s.h.
BIOS:7600 Advanced Biostatistics Seminar 1-3 s.h.
BIOS:6810 Bayesian Methods and Design 3 s.h.
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.
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.
BIOL:4213 Bioinformatics 3 s.h.
IE:4172 Big Data Analysis 3 s.h.
Work with your advisor to select electives.

 


Questions or comments? Contact Lexie Just. This page was last reviewed on August 21, 2019.