This certificate prepares clinicians, health care managers, statisticians, epidemiologists, computer programmers, data analysts, and other professionals in analysis of complex health care data, including data extracted from electronic health records, claims data, and consumer generated data. Since electronic health records and related data repositories are becoming increasing more massive, the certificate emphasizes topics related to big data analysis. Data mining, propensity scoring, and other advanced analytic techniques covered in the certificate, can handle complex problems typically found in observational data: large, multidimensional and multi-type data sets, with many confounding issues and noise. These techniques can be computationally efficient on large scale analysis and intelligent in predicting an outcome.

This graduate certificate may be pursued on a full- or part-time basis.

Admissions

Applicants must hold a bachelor's degree from a regionally-accredited institution and must have a minimum of a 3.0 GPA to be considered. Applicants must meet the admission standards and application requirements specified in Graduate Admissions  and must apply using the online Application for Graduate Admission. The application process is competitive, and applications are considered for the fall and spring semesters. For application deadlines and detailed application requirements, refer to the CHHS Admissions website.

Policies

For policies governing all graduate certificates, see AP.6.8 Requirements for Graduate Certificates.

Banner Code: HH-CERG-HIDA

Certificate Requirements

Total credits: 18

Students must complete all courses with a grade of B or better.  The course content and syllabi are also available at the program website and by contacting hap@gmu.edu.

Required Courses

Students must complete six of the following:18
Statistical Process Control in Healthcare
Introduction to Health Informatics
Health Care Databases
Advanced Statistics in Health Services Research I
Health Data Integration
Health Data Visualization
Data Mining in Health Care
Comparative Effectiveness Analysis using Observational Data
Total Credits18