Bring data, science, and quantitative thinking together to improve health

Whether you are interested in data, mathematics, science, technology, or human health, biostatistics offers a way to use those interests to investigate disease, evaluate treatments, understand populations, and improve health decisions.

But answering a health question takes more than information alone:


  • What patterns are hidden in the data?
  • How confident can we be in what we find?
  • Does the evidence reflect different people and populations?
  • Can the result be reproduced in another study or setting?
  • How can the finding lead to a better decision?

That's where BIOSTATISTICS and HEALTH DATA SCIENCE bring data, quantitative reasoning, and scientific discovery together to improve human health.

Hello Kyle,


Biostatistics is the science of learning from health data. It brings data, mathematics, computing, and the health sciences together to investigate disease, evaluate treatments, understand populations, and improve health decisions.


The goal is not simply to find patterns, but to determine what those patterns mean, how certain we can be, and whether they can help improve people’s lives.


At Michigan Biostatistics, graduate study can help you develop the analytical and scientific tools to transform complex health information into credible evidence—regardless of which academic path first led you to the field.


This combination of quantitative rigor and human impact is what makes biostatistics and health data science distinctive. The goal is not simply to find patterns. It is to understand what those patterns mean, how certain we can be, and whether they can help improve people’s lives.


Biostatisticians work alongside scientists, clinicians, and public health leaders at every stage of discovery. They help determine which questions to ask, how studies should be designed, what conclusions the data can support, and how findings should guide decisions about treatment, prevention, and policy.


That work might include:

Predicting patient outcomes using high-dimensional health data

Identifying genes associated with disease risk

Modeling the progression or spread of disease

Designing an efficient and informative clinical trial

Detecting and addressing bias in health algorithms

Validating emerging biomedical technologies for scientific and clinical use

At the University of Michigan, you can pursue this work through three distinct graduate pathways:


  • MS in Health Data Science, with its strong computational and machine-learning focus
  • MS in Biostatistics, combining rigorous statistical training with applied health research
  • PhD in Biostatistics, preparing students to develop new methods and lead original research.


You may already have the beginnings of a biostatistician’s mindset. If you are curious about science, enjoy working through difficult quantitative problems, or want to use data for a purpose larger than prediction alone, we invite you to discover where this field could take you.

Interested in learning more about biostatistics?
 
 
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