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Anschutz Medical Campus

Master of Science in Biostatistics

University of Colorado Anschutz Medical Campus - Aurora, CO

At the Colorado School of Public Health you can train in the high-demand field of biostatistics on the region's premier health sciences campus - the University of Colorado Anschutz Medical Campus.

Offered through the CU Denver Graduate School and Colorado School of Public Health, the master of science in biostatistics exposes students to a wide variety of studies and research interests including longitudinal data, statistical genetics and genomics, clinical trials, infectious disease, and cancer research. To see the wide range of projects undertaken by our students, you can review our previous thesis titles.

The program targets students with strong skills and training in mathematics with the interest to apply math in health care and biological settings.  Students pursuing the MS look forward to rewarding careers in academia, research, public health and private industry.  Potential positions include design and analysis of health studies, clinical trials, drug development and public health studies.

Coursework includes: applied and theoretical statistics; consulting; analysis of clinical trials; longitudinal and survival data; as well as production of a research paper or thesis.  Please note this program requires 3 semesters of recent calculus (e.g., Calculus I-III offered from a math or engineering department), and linear algebra with a preference for grades of B+ or higher, an introductory statistics or biostatistics course, and exposure to some statistical software (e.g., R, SAS).  Students interested in a less mathematical program with broader exposure to Public Health should consider the MPH in Applied Biostatistics​.

Students interested in specializing in statistical methods in genomics and genetics can consider the MS Minor in Statistical Genomics/Genetics.  Students interested in specializing in the generation, analysis and interpretation of large data sets can consider the MS Minor in Data Science Analytics.

The MS Biostatistics program is designed to be completed in 2 years by full time students.  Many MS students are supported through Research or Teaching Assistantship positions.  These positions are competitive, and offer opportunities for training and experience in research, teaching and practice in Biostatistics.

Program Curriculum

MS Biostatistics Curriculum Checksheet

Course Prefix Course Requirement Semester Credits
Required Biostatistics Courses (20 credits)
BIOS 6611 Biostatistical Methods I


BIOS 6612 Biostatistical Methods II


BIOS 6621 Biostatistical Consulting I


BIOS 6622​ ​Biostatistical Consulting II ​1
BIOS 6624 ​Advanced Statistical Methods and Analysis ​3
BIOS 6631​ ​Statistical Theory I ​3
BIOS 6632​ ​Statistical Theory II ​3
BIOS ​6643 ​Longitudinal Data Analysis 3​
Required Public Health Courses​ (6 Credits)

PUBH 6600​

EHOH 6601

​Foundations in Public Health




EPID 6630​ ​Epidemiology ​3
Elective Biostatistical Courses​ (6 credits from this list)
(courses not listed require director approval)​
BIOS ​6641 ​Causal Inference 3​
BIOS ​6645 ​Predictive Analytics 3​
BIOS 6646​ ​Survival Analysis ​3
BIOS 6649​ ​Design of Studies in Health Sciences ​3
BIOS 6655​ ​Stat. Methods in Genetic Association Studies ​3
BIOS 6660​

BIOS 6640
​Analysis of High-throughput Data

Python and R Programming in Data Science

Thesis/Research Paper/Project​ ​ ​4

Total Program Credits

Note: Students may substitute CBHS 6619 Public Health in the Global Community - 3 credits for the combination of PUBH 6600 and EPID 6601 to fulfill the Public Health requirement.

(Curriculum is subject to change annually)


Program Competencies

Identifier MS Biostatistics Competencies
Study Development: Work collaboratively with biomedical or public health researchers and PhD biostatisticians, as necessary, to provide biostatistical expertise in the development and design of research studies.
Map study aims to testable statistical hypotheses.
Identify the strengths and weaknesses of various clinical trial and observational study designs and the data collection methods that go with these designs.
Use probability and statistical theory to develop appropriate data analysis plans for study hypotheses.
Modeling and Analysis: Develop, carry out and report biostatistical modeling and analysis of biological science and public health studies.
Use advanced techniques for summary and visualization of complex data for exploratory analysis and presentation.
Use probability and statistical theory to identify appropriate modeling and analysis methods to address study hypotheses.
Determine and check modeling assumptions, and verify validity of proposed analyses.
Carry out valid and efficient modeling, estimation, and inference to address study hypotheses, using standard statistical methods including basic one and two sample methods, general linear models including regression and anova, logistic regression, and clustered and longitudinal analysis.
Read biostatistical literature to determine and implement alternate methods of analysis.
Demonstrate statistical programming proficiency, good coding style and use of reproducible research principles in leading statistical software.
Biologic or Public Health Relevance: Show how biostatistical tools apply to and influence research and policy in the biomedical and public health arenas.
MS-BIOS 10  
Read subject specific biomedical or public health literature and synthesize issues that are important in the design, implementation, and analysis of research in the subject area.
Apply basic ethical concepts of public health policy and practice, ensure the quality and security of information used in a study and adhere to the principles of research ethics.
MS-BIOS 12  
Develop and implement specialized study designs and analyses in biological (e.g. genetic association, genomics) or public health (e.g. epidemiological) settings.
Communication: Communicate orally and in writing biostatistical concepts and results to both biostatistical and non-biostatistical audiences.
Communicate orally and in writing simple and complex statistical ideas and methods to collaborators in non-technical terms including preparation of analysis section of grant proposals and methods and results sections of manuscripts.
Manage the preparation of large documents (e.g. grant proposals or manuscripts).

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