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.
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.
For policies governing all graduate certificates, see AP.6.8 Requirements for Graduate Certificates.
Total credits: 18
|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|