Dr. Spencer Huang

The MPH biostatistics degree track builds on classic public health biostatistics skills and knowledge base and takes advantage of the expertise of UWM faculty in the areas of genetics, bioinformatics and big data science.

MPH biostatistics students get the opportunity to learn and apply statistical genetics in the context of complex disease study, high-throughput computing used in big data science, and applications in evidence-based, patient-centered outcome studies.

Program Type

Master’s

Program Format

On Campus
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The Perfect Fit

“I enjoyed economics and I enjoyed math. I thought, how can I find a good match in the health world? And that’s when I learned about public health.

I could take those quantitative skills that I gained through my undergrad, and also my interest in promoting health, on a big scale in the health care world.”

Sarah Laurent, MPH-Biostatistics

Sarah Laurent walking near Lake Michigan in Milwaukee
Biostatistics student Sarah Laurent tells us why she chose a career in public health.

Biostatistics master’s coursework includes topics and material such as:

  • Interpretation of personalized and evidence-based medicine in the context of public health.
  • Basic understanding of genetics and epigenetics.
  • General “omic” approaches and concepts.

MPH Biostatistics (46 credits)

All students enrolled in the MPH program take a common set of core classes. The core curriculum consists of at least 20 credit hours, including at least three credits field experience and a two-credit capstone seminar. In addition to the common core, students complete the required coursework in one of five specialization tracks. Students must maintain a cumulative GPA of 3.0 or better in order to progress through the program.

MPH Required Common Core Courses (at least 24-25 credits)

PH 702: Introduction to Biostatistics (3 credits)
PH 703: Environmental Health Sciences (3 credits)
PH 704: Principles and Methods of Epidemiology (3 credits)
PH 705: Public Health Policy and Administration (3 credits)
PH 706: Perspectives in Community and Behavioral Health (3 credits)
PH 708: Health Systems and Population Health (3 credits)
PH 733: Overview of Qualitative Methods for Public Health (1 credit)
PH 790: Field Experience in Public Health (3 credits)
PH 791: Leadership in Public Health (1 credit)
PH 800: Capstone in Public Health (2 credits)

Required Courses (9 credits)

PH 711: Intermediate Biostatistics (3 credits)
PH 712: Probability and Statistical Inference (3 credits)
PH 718: Data Management and Visualization in R (3 credits)

“S”electives – Choose four (12 credits minimum)

PH 707: Introduction to Statistical Computing (1 credit)
PH 709: Public Health Informatics (3 credits)
PH 713: Analyzing Observational and Experimental Data (3 credits)
PH 714: Statistical Genetics and Genetic Epidemiology (3 credits)
PH 715: Applied Categorical Data Analysis (3 credits)
PH 716: Applied Survival Analysis (3 credits)
PH 717: Applied Longitudinal Data Analysis (3 credits)
PH 720: Special Topics in Biostatistics and Bioinformatics (1 – 3 credits)
PH 721: Introduction to Translational Bioinformatics (3 credits)
PH 723: Clinical Trials (3 credits)

Please note: All courses are subject to change. Please consult the Academic Catalog for the most up-to-date information.

Biostatistics track competencies

  1. Function as a collaborator with community partners on public health projects and in developing recommendations for appropriate study designs that advance social justice and population health.
  2. Translate research objectives into testable hypotheses.
  3. Differentiate between quantitative problems that can be addressed with routine methods and those requiring input from a doctoral-level biostatistician.
  4. Demonstrate a broad knowledge and understanding of statistical techniques used in public health studies and health-related scientific investigations.
  5. Identify and apply a variety of appropriate statistical methods for developing inferences about public health-related questions.
  6. Demonstrate basic programming skills in multiple statistical software packages and data management and integration techniques for public health and big data projects.
  7. Interpret and critique statistical analyses in publications for public health professionals.
  8. Demonstrate a cognizance of the social, environmental and public health contexts that are impacted by the results of statistical analyses.
  9. Demonstrate effective written and oral communication skills when reporting statistical results to different audiences of public health professionals, policy makers and community partners.
  10. Formulate and produce graphical displays of quantitative information (e.g., scatter plots, box plots and line graphs) that effectively communicate analytic findings.
  11. Differentiate between ethical and unethical reporting of data and results.

Careers in Biostatistics

Biostatisticians work in hospitals and for health insurance systems, pharmaceutical companies, companies that produce health-related products and health nonprofits. Job prospects for new graduates with biostatistics master’s degrees are excellent, with typical starting annual salaries as high as $65,000, according to the American Statistical Association.

Chiang-Ching Huang
  • Professor, Biostatistics

Faculty Research Interests

  • Genetic determinants of common chronic disease (including heart disease, bleeding disorders, stroke and colorectal cancer).
  • Using genomic technologies and bioinformatic and biostatistical techniques to accurately predict risk and treatment response in cancer and cardiovascular disease.
  • Major molecular mechanisms and pathways that model disease progression.
  • Using biomedical informatics, mathematical modeling and simulations to characterize and predict the use of genetics in medical practice and, in particular, pathology.
  • Use of high-throughput genetic technologies, such as micro-arrays and next generation sequencers in the discovery and applications of genetics to complex diseases and environmental-gene development pathways.
  • Statistical methods and computational tools to identify genetic variants that influence the susceptibility to complex diseases such as cancer of the breast, colon/rectum, lung and prostate.