Join an emerging field with a lot of opportunities
The world of big data and its application to health care is a growing field. People equipped to work in it will find job opportunities in medical, academic, government, industrial, and population health settings. A master’s degree in this field opens the door to high-paying jobs and in-demand positions.
About the program
The Master of Science (MS) in Biostatistics and Data Science degree is a two-year program that prepares graduates to extract, analyze, and translate vast amounts of data into actionable evidence, and communicate their findings to collaborators in other disciplines. This program combines competencies in statistics, computer science, and epidemiology, a crucial set of skills for analyzing increasingly complex health-related data.
Graduates will gain competence in:
- Fundamental statistical theory.
- Common methods in biostatistics (regression, survival, and longitudinal analyses).
- Statistical and computer programming languages (R, SAS, Stata, Python, SQL).
- Artificial intelligence methods like machine learning, data visualization, and databases.
The program's primary objective is to graduate leaders in statistical theory, practical data analysis, big data management and manipulation, and communication skills. All biostatisticians and data scientists must master these competencies to support basic science, clinical, and population health studies.
Gain real-world experience
In addition to coursework, students develop their technical and collaborative skills in supervised consulting sessions and an internship. These hands-on experiences give graduates the background they need to excel, wherever their careers take them.
Students will have ample opportunities to work with high-quality data and reputable researchers from two epidemiologic studies supported by the National Institutes of Health. These include the Jackson Heart Study (JHS), the largest-ever single-site study of cardiovascular disease and its causes in African Americans, and the Atherosclerosis Risk in Communities study (ARIC), which investigates the causes of atherosclerosis and its clinical outcomes, as well as the variation in cardiovascular risk factors and disease by race, gender, and location.
Graduates of the program will be able to:
- Efficiently collect, clean, organize, and analyze biomedical, clinical, and population health data.
- Use standard statistical (R, SAS, and Stata) and computer (Python) programming languages to reproducibly explore and visualize data, fit models, conduct inference, and translate analysis results.
- Conduct all facets of big data analysis, including extracting, storing, manipulating, and analyzing massive genetic and bioinformatics datasets.
- Convert information contained in databases and data warehouses into actionable findings using machine learning and other data science techniques.
- Adhere to rigorous ethical and methodological standards when analyzing real-world data.
- Collaborate with researchers and communicate findings to improve health care and prevent disease.