The Master of Biostatistics program requires a total of 50 credits. Forty-four credits of graded coursework, a Practicum experience, a Proficiency Examination, and a Master’s Project, for which 6 credits are awarded.
Tracks and Timeline
There are four tracks in the Master of Biostatistics program:
This track is designed for students who want to apply biostatistical methods in public health and medicine, focusing on the design and analysis of observational studies and clinical trials. It combines rigorous training in core statistical knowledge with an emphasis on collaboration and communication, preparing graduates to work effectively on interdisciplinary scientific teams.
What Makes This Track Unique?
- Specialized coursework in clinical and translational research methods
- Opportunities to serve as the biostatistician on collaborative research projects
- Strong connections to Duke’s School of Medicine, offering access to diverse biomedical research initiatives
Research Opportunities
Students often work under faculty guidance on real-world projects such as:
- Enhancing MRI sensitivity for breast cancer survivors
- Analyzing the impact of smoking on community lifespan
- Supporting studies in biology, medicine, and biomedical informatics
Common Job Titles:
- Biostatistician
- Clinical Research Data Analyst
Path to Doctoral Study
This track provides a strong foundation for PhD programs in:
- Biostatistics and Statistics (applied or hybrid focus)
- Epidemiology, Computational Biology, Population Health Sciences
Your advanced training in applied statistics sets you apart in competitive doctoral admissions.
The Biomedical Data Science (BDS) Track is available in Year 2 of the Master of Biostatistics Program and is designed for students interested in the intersection of computer science, data science, and statistics—with a strong focus on applications in health research.
This track emphasizes computational skills and covers:
- Workflow best practices for large-scale data projects
- Software tools for managing biomedical datasets
- Data structures and algorithms for data-heavy applications
While Health AI is not the primary focus, BDS students may take electives in Health AI with approval from the Director of Graduate Studies. Unlike the Health AI track, no prior data science experience is required—students join BDS in their second year after completing foundational coursework.
Research Opportunities
Students in the BDS track often work on projects involving:
- Electronic Health Records (EHR)
- Data from wearable devices
- Genomic and molecular datasets
Research typically involves observational studies, causal inference, and machine learning techniques. While most projects are applied, there are also opportunities to develop new computational methods.
Career Outcomes
Graduates of the BDS track are prepared for roles in:
- Academic medical centers
- Pharmaceutical companies
- Contract research organizations (CROs)
- Healthcare systems
- Technology companies in medicine and public health
Common job titles:
- Data Scientist
- Data Analyst
- Statistical Analyst
- Bioinformatician
- AI/ML Engineer
Path to Doctoral Study
Depending on your focus, you’ll be well-prepared for PhD programs in:
- Biostatistics
- Computational Biology
- Bioinformatics
- Genomics
- Computational Epidemiology
- Computer Science
The Health AI Track is designed for students who want to develop, evaluate, and deploy AI tools in healthcare or pursue advanced doctoral training in AI methodology. This track emphasizes:
- Machine learning fundamentals
- Managing complex health datasets
- Applying AI solutions in clinical environments
Important: The Health AI Track must be selected during application and enrollment, as some required courses begin in the first semester.
Ideal Candidate Profile
Competitive applicants have:
- Strong quantitative and computational skills
- Prior training in data science and programming
- Practical experience in research or professional settings (recommended)
Course Sequence
- First Semester: Two required data science courses (with some cross-track students as electives)
- Electives: Advanced courses in topics such as:
- Reinforcement learning
- High-dimensional data analysis
- Natural language processing
- Final Semester: Ethical Evaluation and Governance of Clinical AI in Healthcare
- Capstone Project: Apply AI methods to real-world health challenges
Research Opportunities
Work with faculty on cutting-edge AI projects, including:
- Large language models
- Predictive modeling and clinical decision support
- Reinforcement learning and causal machine learning
- Imaging, NLP, and wearable data analysis
Applications span clinical domains (mental health, cardiology, women’s health) and basic science (protein modeling, single-cell sequencing). Students often publish research, present at conferences, and implement algorithms within the Duke Health System.
Career Outcomes
Graduates are prepared for roles in:
- Health tech companies and startups
- Medical imaging and diagnostics firms
- AI divisions of major tech companies
- Hospitals and health systems developing AI solutions
Common job titles:
- Machine Learning Scientist
- Health Informatics Data Scientist
- Predictive Analytics Specialist
- Applied ML Engineer
- AI Research Scientist
Doctoral Pathways
Ideal PhD programs include:
- Biostatistics with AI/ML emphasis
- Computer Science
- Biomedical or Health Informatics
- Computational Data Science
- Engineering programs focused on biomedical or computer engineering
How It Differs from the Biomedical Data Science Track
The BDS Track offers broader biostatistics training with a focus on data science methods, making it ideal for students seeking general biostatistics expertise or those with less prior computing experience. Health AI is more specialized and requires strong programming and data science skills from the start.
The Mathematical Statistics Track is available in Year 2 of the Master of Biostatistics Program and is designed for students who enjoy mathematical theory, proofs, and the development of new statistical methods. Unlike other tracks that focus on applied statistics, this track emphasizes the theoretical and methodological foundations of the field.
Students typically have a strong undergraduate background in mathematics and may:
- Plan to pursue doctoral training in statistics, applied mathematics, or biostatistics
- Seek careers that leverage advanced statistical theory
- Simply enjoy the mathematical underpinnings of biostatistics
Research Opportunities
Students collaborate with faculty on theoretical research projects, such as:
- Comparing statistical methods using mathematical and simulation-based approaches
- Developing new theoretical methodologies
Career Outcomes
Graduates of this track are highly valued in organizations that focus on statistical methodology, including:
- Academia
- Technology companies
- Insurance and finance
- Pharmaceuticals and biotechnology
- Government agencies
Common job titles:
- Statistician
- Quantitative Research Scientist
- Statistical Consultant
- Statistical Modeling Analyst
- Methodological Statistician
- Mathematical Statistician
Doctoral Pathways
Ideal PhD programs for graduates include:
- Statistics
- Biostatistics
- Applied Mathematics
- Quantitative Sciences
Practicum
All Master of Biostatistics students must complete a Practicum, providing hands-on experience that strengthens:
- Analytical skills
- Biological understanding
- Communication abilities
Most students complete the Practicum during the summer after Year 1, but it can be scheduled in Year 2 if needed.
Proficiency Examination
Students must pass a written Proficiency Exam demonstrating mastery of core concepts from first-year courses (BIOSTAT 701–706).
- Taken after Year 1 and before starting electives
Master's Project
A two-semester capstone (6 credits) completed in Year 2.
- Demonstrates mastery of statistical concepts and biostatistics practice
Note: The Core foundational courses are required of all degree-seeking students. Full-time Master of Biostatistics students will select six three-credit elective courses during the second year of study.
Course Planning
First Year
26 graded coursework credit hours
|
Fall |
Spring |
|---|---|
|
BIOSTAT 701 (3) or |
BIOSTAT 704 (3) or |
|
BIOSTAT 702 (3) or OR BIOSTAT 707** (3) |
BIOSTAT 705 (3) or OR BIOSTAT 826** (3) |
|
BIOSTAT 703 (3) or |
BIOSTAT 706 (3) or |
|
BIOSTAT 703L (0) |
BIOSTAT 722 (3) or OR BIOSTAT 821*** (3) |
|
BIOSTAT 721 (3) or |
BIOSTAT 802 (1) |
|
BIOSTAT 801 (1) |
|
|
Total: 13 credit hours |
Total: 13 credit hours |
Summer
- Master's Proficiency Examination (Required)
Covers content from the first and second semesters of training. - Practicum (Required)
May be completed at any point after the first year. - Internship (Optional)
Students participating in a summer external internship are required to enroll in BIOSTAT 829 (1). This course is permission only.
*Students must choose 1 of these 3 courses to complete their Theory Sequence requirements. Any course not selected can be taken for elective credit in the second year with approval from the director of graduate studies.
**Required for Health AI Track to complete the Applied Data Analysis sequence. Students in other tracks may take these courses as electives in their second year with approval from the director of graduate studies.
***To meet program requirements for the Computing sequence, students must take either BIOSTAT 722/722A or BIOSTAT 821.
Second Year
Second Year (24 credit hours total)
[BIOSTAT 720 - Master’s Project and Statistical Consulting (6 credit hours) plus 18 credit hours of graded coursework]
|
Fall |
Spring |
|---|---|
|
BIOSTAT 720 (3) + 3 of the following:
|
BIOSTAT 720 (3)
+ 3 of the following:
*Students on the Health AI Track are required |
|
Total: 3 required credit hours plus 9 elective credit hours |
Total: 3 required credit hours plus 9 elective credit hours |
Other Duke Departments
- Biology: BIOLOGY 790S: Graphic Design for Biologists (3)
- Computational Biology: Any 500 or 600 level except: 510S, 511, or 591
- Computer Science: Any 500 or 600 level
- Electrical and Computer Engineering: Any 500 or 600 level
- Environmental Sciences:
- ENVIRON 537: Environmental Health and Epidemiology (3)
- ENVIRON 761: Geospatial Analysis for Land and Water Mgmt (3)
- **All NSOE courses require NSOE department approval in addition to that of the student’s director of graduate study and the course instructor before submitting the NP-1 form.
- Global Health: GLHLTH 562: Data Science and Data Visualization (3)
- Math:
- MATH 531: Real Analysis I (3)
- MATH 574: Quantitative Methods for Biomedical Data (3)
- MATH 721: Linear Algebra and Application (3)
- MATH 731: Introduction to Real Analysis (3)
- MATH 765: Introduction to High Dimensional Data Analysis (3)
- MATH 766: Statistical Learning and Bayesian Nonparametrics (3)
- Statistical Sciences:
- Any 500 level except 501S
- Any 600 level except 693
- Any 700 level except 701S, 790, 791
- Any 800 level except 851
- Any 900 level except 993, 994, 995
First Year
26 graded coursework credit hours
|
Fall |
Spring |
|---|---|
|
BIOSTAT 701 (3) or |
BIOSTAT 704 (3) or |
|
BIOSTAT 702 (3) or |
BIOSTAT 705 (3) or |
|
BIOSTAT 703 (3) or |
BIOSTAT 706 (3) or |
|
BIOSTAT 703L (0) |
BIOSTAT 722 (3) or |
|
BIOSTAT 721 (3) or |
BIOSTAT 802 (1) |
|
BIOSTAT 801 (1) |
N/A |
|
Total: 13 credit hours |
Total: 13 credit hours |
Summer
- Master’s Proficiency Examination (covers content from BIOSTAT 701-706)
- Practicum (may be completed at any point after the first year)
Second Year
24 credit hours – Master’s project (6) plus graded coursework credit hours (18)
|
Fall |
Spring |
|---|---|
|
BIOSTAT 720 (3) |
BIOSTAT 720 (3) |
|
+ 3 of the following:
|
+ 3 of the following:
|
|
Biology (3): Computational Biology (3): Computer Science (3): Environmental (3): Global Health (3): Statistical Science (3): |
Biology (3): Computational Biology (3): Computer Science (3): Environmental (3): Global Health (3): Statistical Science (3): |
|
Total: 3 required credit hours plus 9 elective credit hours |
Total: 3 required credit hours plus 9 elective credit hours |
Policy for Substitution of Core Course Requirements
Students who have relevant pre-matriculation coursework or training may request to meet the core course requirements for the first year Statistical Theory and Computing course sequences by taking more advanced graduate-level courses in related areas for equivalent credit. Advanced courses that may be selected are: BIOSTAT 732 (3 credits), BIOSTAT 828, BIOSTAT 906, STA 532, STA 640, STA 732, and ECE 551D. Testing or other evaluation may be required prior to approval. Instructor permission may be required for enrollment in some courses. Requests must be submitted to the Director of Graduate Studies either 2 weeks prior to the start of the fall semester (for courses to be taken in the fall) or no later than the last day of the fall semester (for courses to be taken in the spring). Approval is required from the Director of Graduate Studies.