Biostatistics and Health Analytics, M.S.
Saint Louis University
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Study the social, environmental and physical influences that together determine the health and well-being of people and communities.
Ninety-four percent of recent SLU graduates report being satisfied with their careers, volunteer work or graduate studies.
The 21st century is the era of "big data." Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. It is estimated that 30% of this data comes from the health care industry. Saint Louis University's Master of Science (M.S.) in Biostatistics and Health Analytics will not only prepare students to handle this data but also to apply analytic techniques to answer important research questions related to health and health care.
This program is designed for students interested in a field that combines quantitative reasoning, coding, and scientific skills to solve problems in health and medicine. It is suited for those with strong quantitative abilities and a desire to apply mathematics, statistics, computer programming and data analysis to health-related issues. An M.S. in Biostatistics and Health Analytics can prepare students for professional biostatistical careers and provides a firm academic foundation for subsequent doctoral study in statistical science.
The field of biostatistics is a Science Technology Engineering and Mathematics (STEM) focus area since the field of biostatistics is a mathematically based science. In 2006, the United States launched a program to increase the number of students who receive training in STEM areas. This program will fill the need for graduates with technical abilities to analyze data and draw inferences.
Students with interest in learning skills across a broad spectrum of biostatistics and data analytics can choose the traditional Biostatistics Concentration. Those who want to apply their skills on geospatial data can choose the Geospatial Health Data Analytics Concentration. Both programs require a core set of material on biostatistics and analytics, and then each concentration has its own requirements for completion.
Students take courses in public health, the theory of biostatistics, methods of biostatistics and computing. Students finish by doing a capstone project under the direction of a faculty member in the Department of Epidemiology and Biostatistics.
Students will have the opportunity to do research as part of their capstone project
Graduates of SLU's M.S. in Biostatistics and Health Analytics will be prepared to work as a biostatistician, data scientist or data analyst. The number of students in the U.S. who have received master’s degrees in biostatistics has increased by a factor of seven since 2000.
Data scientists, biostatisticians and statisticians are often rated as among the nation's top jobs, measured in terms of salary and job satisfaction.
The College for Public Health and Social Justice offers several ways to help finance graduate education. Opportunities include a limited number of merit-based scholarships and graduate research assistantships. Awards are made to applicants with the highest combinations of GPAs and test scores who complete their applications by the priority deadlines.
For more information, visit the student financial services office online at http://finaid.slu.edu.
The College for Public Health and Social Justice is fully accredited by the Council on Education for Public Health (CEPH).
Most recent CEPH Self-Study - July 2016
Learning Outcomes Common to Both Concentrations
Additional Learning Outcomes for Traditional Biostatistics Concentration
Additional Learning Outcomes for Geospatial and Health Data Analytics Concentration
| Code | Title | Credits |
|---|---|---|
| Required Core Courses | ||
| BST 5020 | Theory of Biostatistics | 3 |
| BST 5025 | Theory of Biostatistics II | 3 |
| BST 5100 | Introduction to General Linear Modeling | 3 |
| BST 5400 | Applied Data Management | 3 |
| PUBH 5030 | Methodological Approaches to Understanding Population Health | 3 |
| BST 5961 | Master's Project | 3 |
| Concentrations | ||
| Select one of the following: | 12 | |
| Electives | ||
| Select two courses from among the following: | 6 | |
| Multilevel and Longitudinal Data Analysis | ||
| Bayesian Statistics | ||
| Sampling Theory and Survey Design in Public Health | ||
| Causal Inference | ||
| Geospatial Data Management | ||
| Spatial Demography: Applied Statistics for Spatial Data | ||
| Geospatial Analytics | ||
| Total Credits | 36 | |
| Code | Title | Credits |
|---|---|---|
| BST 5030 | Statistical Programming and Study Planning: SAS | 3 |
| BST 5200 | Survival Data Analysis | 3 |
| BST 5210 | Categorical Data Analysis | 3 |
| Elective Courses | ||
| Choose two electives in consultation with mentor | 6 | |
| Code | Title | Credits |
|---|---|---|
| GIS 5010 | Introduction to Geographic Information Systems | 3 |
| BST 5600 | R for Spatial Analysis | 3 |
| BST 5610 | Spatial Epidemiology and Disease Mapping | 3 |
| BST 5620 | Spatio-Temporal Models in Public Health | 3 |
| Elective Courses | ||
| Choose two electives in consultation with mentor | 6 | |
Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.
Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.
This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.
| Year One | ||
|---|---|---|
| Fall | Credits | |
| Critical course: BST 5020 | Theory of Biostatistics | 3 |
| Critical course: PUBH 5030 | Methodological Approaches to Understanding Population Health | 3 |
| Critical course: GIS 5010 | Introduction to Geographic Information Systems | 3 |
| Credits | 9 | |
| Spring | ||
| Critical course: BST 5025 | Theory of Biostatistics II | 3 |
| Critical course: BST 5100 | Introduction to General Linear Modeling | 3 |
| Critical course: BST 5600 | R for Spatial Analysis | 3 |
| Credits | 9 | |
| Year Two | ||
| Fall | ||
| Critical course: BST 5610 | Spatial Epidemiology and Disease Mapping | 3 |
| Critical course: BST 5400 | Applied Data Management | 3 |
| Elective | Biostatistics Elective chosen in consultation with mentor | 3 |
| Credits | 9 | |
| Spring | ||
| Critical course: BST 5620 | Spatio-Temporal Models in Public Health | 3 |
| Critical course: BST 5961 | Master's Project | 3 |
| Elective | Biostatistics Elective chosen in consultation with mentor | 3 |
| Credits | 9 | |
| Total Credits | 36 | |
| Year One | ||
|---|---|---|
| Fall | Credits | |
| Critical course: BST 5020 | Theory of Biostatistics | 3 |
| Critical course: PUBH 5030 | Methodological Approaches to Understanding Population Health | 3 |
| Critical course: BST 5400 | Applied Data Management | 3 |
| Credits | 9 | |
| Spring | ||
| Critical course: BST 5025 | Theory of Biostatistics II | 3 |
| Critical course: BST 5030 | Statistical Programming and Study Planning: SAS | 3 |
| Critical course: BST 5100 | Introduction to General Linear Modeling | 3 |
| Credits | 9 | |
| Year Two | ||
| Fall | ||
| Critical course: BST 5200 | Survival Data Analysis | 3 |
| Critical course: BST 5210 | Categorical Data Analysis | 3 |
| Elective | Biostatistics Elective chosen in consultation with mentor | 3 |
| Credits | 9 | |
| Spring | ||
| Critical course: BST 5961 | Master's Project | 3 |
| (BST 5500????) | ||
| Elective | Biostatistics Elective chosen in consultation with mentor | 3 |
| Credits | 6 | |
| Total Credits | 33 | |