Biostatistics and Health Analytics, M.S.
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.Ìý91Å®Éñ's Master of Science (M.S.) in Biostatistics and Health Analytics will not only prepare students to handle this data but also apply analytic techniques to answer important research questions related to health and health care.
Program Highlights
- 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 withÌýstrong 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.Ìý
Curriculum Overview
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.
Two Concentrations Available
Students interested 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 to 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.
Fieldwork and Research Opportunities
Students will have the opportunity to do research as part of their capstone project.
Careers
Graduates of 91Å®Éñ's M.S. in Biostatistics and Health Analytics will be prepared to work as biostatisticians, data scientists or data analysts. The number of students in the U.S. who have received master’s degrees in biostatistics has increased by seven since 2000.Ìý
Data scientists, biostatisticians and statisticians are often rated as among the nation's top jobs, measured in salary and job satisfaction.
Admission Requirements
Applicants should have a bachelor's degree in a science-related field, such as chemistry, biology, physics, mathematics, engineering, etc., with an overall GPA of 2.5 or higher. At least one semester of calculus and one introductory statistics course are required.
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Tuition | Cost Per Credit |
---|---|
Graduate Tuition | $1,370 |
Additional charges may apply. Other resources are listed below:
Information on Tuition and Fees
Scholarships and Financial Aid
The College for Public Health and Social Justice offers several ways to help finance graduate education.ÌýÌýOpportunities include a limited number of merit-based scholarships and graduate research assistantships. ÌýAwards 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 Office of Student Financial Services.
Accreditation
91Å®Éñ's College for Public Health and Social Justice is fully accredited by the Council on Education for Public Health (CEPH). To see our most recent accreditation documentation, please visit theÌýCollege for Public Health and Social Justice website.
Learning Outcomes Common to Both Concentrations
- Foundations: Students should be able to apply foundational principles of probability and statistics to develop methods for estimation and hypothesis testing.
- Analysis: Students will apply advanced statistical methods to analyze data and make inferences to answer research questions in public health.
- Communication:ÌýStudents will describe the process of data collection, the application of statistical methodology, and the results of statistical analysis orally and in writing.
Additional Learning Outcomes for Traditional Biostatistics ConcentrationÌý
- Data and computing: Students will apply the appropriate software to collect, store, manage, clean and analyze data.
- Design: Students should be able to design experiments or data collection strategies, including sample size requirements, to answer research questions in public health.
Additional Learning Outcomes for Geospatial and Health Data Analytics Concentration
- Data management:ÌýStudents will acquire, manage, analyze, and display geospatial health data.Ìý
- Spatial and Spatio-temporal inference: Students will build and analyze models to assess the health of populations across both time and geographic regions.
Code | Title | Credits |
---|---|---|
Required Core Courses | ||
µþ³§°ÕÌý5020 | Theory of Biostatistics | 3 |
µþ³§°ÕÌý5025 | Theory of Biostatistics II | 3 |
µþ³§°ÕÌý5100 | Introduction to General Linear Modeling | 3 |
µþ³§°ÕÌý5400 | Applied Data Management | 3 |
±Ê±«µþ±áÌý5010 | Mission and Practice of Global Public Health | 2 |
±Ê±«µþ±áÌý5030 | Methodological Approaches to Understanding Population Health | 3 |
µþ³§°ÕÌý5961 | Master's Project | 3 |
Concentrations | 12 | |
Select one of the following: | ||
Elective | 3 | |
Total Credits | 35 |
Continuation Standards
Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.
Electives
Code | Title | Credits |
---|---|---|
µþ³§°ÕÌý5220 | Multilevel and Longitudinal Data Analysis | 3 |
µþ³§°ÕÌý5230 | Bayesian Statistics | 3 |
µþ³§°ÕÌý5420 | Sampling Theory and Survey Design in Public Health | 3 |
µþ³§°ÕÌý6100 | Causal Inference | 3 |
³Ò±õ³§Ìý5030 | Geospatial Data Management | 3 |
³§°¿°äÌý5670 | Spatial Demography – Applied Spatial Statistics | 3 |
³Ò±õ³§Ìý5120 | Geospatial Analytics | 3 |
Traditional Biostatistics Concentration
Code | Title | Credits |
---|---|---|
µþ³§°ÕÌý5030 | Statistical Programming and Study Planning: SAS | 3 |
µþ³§°ÕÌý5200 | Survival Data Analysis | 3 |
µþ³§°ÕÌý5210 | Categorical Data Analysis | 3 |
µþ³§°ÕÌý5500 | Statistical Learning | 3 |
Total Credits | 12 |
Geospatial Health Data Analytics Concentration
Code | Title | Credits |
---|---|---|
³Ò±õ³§Ìý5010 | Introduction to Geographic Information Systems | 3 |
µþ³§°ÕÌý5600 | R for Spatial Analysis | 3 |
µþ³§°ÕÌý5610 | Spatial Epidemiology and Disease Mapping | 3 |
µþ³§°ÕÌý5450 | Data Visualization | 3 |
Total Credits | 12 |
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollmentÌýunless otherwise noted. Ìý
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.
Geospatial Health Data Analytics Concentration
Year One | ||
---|---|---|
Fall | Credits | |
µþ³§°ÕÌý5020 | Theory of Biostatistics | 3 |
µþ³§°ÕÌý5400 | Applied Data Management | 3 |
µþ³§°ÕÌý5450 | Data Visualization | 3 |
Ìý | Credits | 9 |
Spring | ||
µþ³§°ÕÌý5025 | Theory of Biostatistics II | 3 |
µþ³§°ÕÌý5100 | Introduction to General Linear Modeling | 3 |
µþ³§°ÕÌý5600 | R for Spatial Analysis | 3 |
±Ê±«µþ±áÌý5030 | Methodological Approaches to Understanding Population Health | 3 |
Ìý | Credits | 12 |
Year Two | ||
Fall | ||
µþ³§°ÕÌý5610 | Spatial Epidemiology and Disease Mapping | 3 |
±Ê±«µþ±áÌý5010 | Mission and Practice of Global Public Health | 2 |
BST 5XXX | Biostatistics Elective | 3 |
Ìý | Credits | 8 |
Spring | ||
³Ò±õ³§Ìý5010 | Introduction to Geographic Information Systems | 3 |
µþ³§°ÕÌý5961 | Master's Project | 3 |
Ìý | Credits | 6 |
Ìý | Total Credits | 35 |
Traditional Biostatistics and Health Analytics ConcentrationÌý
Year One | ||
---|---|---|
Fall | Credits | |
µþ³§°ÕÌý5020 | Theory of Biostatistics | 3 |
µþ³§°ÕÌý5400 | Applied Data Management | 3 |
µþ³§°ÕÌý5030 | Statistical Programming and Study Planning: SAS | 3 |
Ìý | Credits | 9 |
Spring | ||
µþ³§°ÕÌý5025 | Theory of Biostatistics II | 3 |
µþ³§°ÕÌý5100 | Introduction to General Linear Modeling | 3 |
±Ê±«µþ±áÌý5030 | Methodological Approaches to Understanding Population Health | 3 |
Ìý | Credits | 9 |
Year Two | ||
Fall | ||
µþ³§°ÕÌý5200 | Survival Data Analysis | 3 |
µþ³§°ÕÌý5210 | Categorical Data Analysis | 3 |
µþ³§°ÕÌý5500 | Statistical Learning | 3 |
Ìý | Credits | 9 |
Spring | ||
µþ³§°ÕÌý5961 | Master's Project | 3 |
±Ê±«µþ±áÌý5010 | Mission and Practice of Global Public Health | 2 |
Elective | Biostatistics Elective chosen in consultation with mentor | 3 |
Ìý | Credits | 8 |
Ìý | Total Credits | 35 |
For additional admission questions please contact:
Bernie Backer
Director of Graduate Recruitment and Admissions
314-977-8144
bernard.backer@slu.edu