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Data Mining Track
Data Mining Certificate

Data Mining Track
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All applicants to the M.S. program are required to take either the Graduate Record Examination (GRE) or the Graduate Management Admission Test (GMAT). Minimum requirements in order to be considered for admission are the standard university criteria of a grade point average (GPA) of 3.0 for the last 60 attempted semester hours of credit earned toward the baccalaureate or a competitive GRE score on the combined verbal-quantitative sections of the General (Aptitude) Test or a competitive GMAT score. The GRE/GMAT score must be less than five years old. International students and students whose native language is not English must score at least 220 (computer-based test; or equivalent score on the paper-based test) on the Test of English as a Foreign Language (TOEFL).

Students entering the graduate program should have a good working knowledge of at least one programming language and should have taken undergraduate courses in calculus and statistical methods. An undergraduate course in matrices or linear algebra is also required except for those students in the Actuarial Science track or the Data Mining track. Those students who are not adequately prepared in these areas may need to complete some undergraduate course work before beginning their graduate program. Applicants not qualified for regular graduate status may be initially admitted to the university in non-degree-seeking status and later admitted to regular status once all deficiencies have been eliminated, although only nine hours of graduate course work taken as a non-degree-seeking student can count toward a graduate degree.

Meeting minimum UCF admission criteria does not guarantee program admission. Final admission is based on evaluation of the applicant's abilities, past performance, recommendations, match of this program and faculty expertise to the applicant's career/academic goals, and the applicant's potential for completing the degree.


"Data Mining is an exciting and evolving discipline leading to the discovery of previously unknown patterns in data for potential business advantage"

 

       
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