Admission Requirements
Applicants must meet the general Graduate School requirements for admission to master’s degree programs. Applications must include all of the materials listed by the Graduate School as typical for master’s degree application submissions. In addition to the general requirements for admission to the Graduate School, an earned baccalaureate degree from a college or university accredited by an accepted accrediting body in computer sciences, health sciences, information systems, or life sciences or in an informatics discipline or a closely related field is required for study toward the M.S. in Heath Informatics and Analytics. Acceptable scores on the verbal, quantitative, and analytical sections of the GRE are required.
GRE Waiver
The GRE requirement may be waived for applicants who meet one of the following:
- Hold a terminal degree (e.g., J.D., M.D., D.D.S., or Ph.D.)
- Have a cumulative undergraduate or graduate GPA of 3.0 and above from a college or university accredited by an accepted accredited body
- Have successfully completed a minimum of two of the required courses in the Graduate Certificate in Health Informatics and Analytics with a GPA of 3.5 or above, and a letter of recommendation from at least one of the HIA course instructors
Waiver must be requested from the HIA Graduate Program Director when submitting the completed application. All waivers are at the discretion of the HIA Graduate Program Director. Applicants satisfying one of the above criteria may be asked by the HIA Graduate Program Director to report GRE scores.
Early Entry Program
Exceptional undergraduate students at UNC Charlotte may apply for the Early Entry Program and begin work toward the graduate degree before completion of the baccalaureate degree. See the Undergraduate Catalog for details and requirements. Also see the Degree Requirements and Academic Policies section of the Graduate Catalog for more information about Early Entry Programs.
Degree Requirements
The M.S. in Health Informatics and Analytics program requires 36 graduate credit hours, including 6 credit hours of Foundational Core courses, 12 credit hours of Core courses, 9 hours of Selective courses, 6 credit hours of Restricted Elective courses, and 3 credit hours of a Culminating Experience that can be satisfied by either an HCIP 6400 Internship or HCIP 6250 Problem Solving in Healthcare Analytics course.
A maximum of 6 hours of graduate credit may be transferred. Students may apply all of the credits earned in the Graduate Certificate in Health Informatics and Analytics towards the M.S. in Health Informatics and Analytics.
The M.S. courses that also serve the Graduate Certificate in Health Informatics and Analytics program are available via online delivery as well as face-to-face formats, meaning much (but not all) of the M.S. program is available in an online format.
Applicants lacking a college-level statistics course within 5 years of matriculation may be required to take HADM 6108 or a comparable course upon entering the program. This course would not count toward degree requirements.
By the end of the first semester of matriculation in the program, students must complete (or be excused from based upon prior training and/or experience) non-credit asynchronous training modules in computer vocabularies, programming systems, health vocabularies, and classification systems.
Upon entering into the HIA program, students shall choose in consultation with the Program Director one of the two concentrations, the Health Services Outcomes (HSO) Concentration or the Data Science (DS) Concentration, based on their prior training and experience. These concentrations are designed to provide guidance for course selection and career planning. The HSO Concentration is most suitable for students with a health-related background, while the DS Concentration assumes more background in computing and statistics. Each concentration has its own selection of Foundational Core, Core, and Selective courses. They share the same broad selection of Restrictive Elective courses.
Students who choose the Data Science Concentration should have current working knowledge of at least one high-level programming language (e.g., Python, Java, R, or C/C++/C#); and a familiarity with computer systems and applications. A minimal background in mathematics, including two semesters of calculus and one semester of statistics, are recommended. One course in linear algebra is also highly desirable.
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