Admissions Criteria Admission to the program is contingent upon an assessment of the candidate’s ability to successfully pursue graduate study. This assessment will be made by examining previous academic performance, letters of recommendation, the applicant’s essay, work experience, performance on standardized exams (such as the GRE), and any other evidence that the admissions committee believes to be relevant. Applicants must submit the following evidence of their ability to pursue graduate study: 1.A baccalaureate degree from a regionally accredited college or university. Transcripts from each institution attended must be submitted even if a degree was not conferred. 2.A record of scholarly achievement at the undergraduate level. Applicants are expected to have a 3.0 (based on a 4.0 scale) cumulative undergraduate grade point average, and a 3.0 in their major field of study. An applicant whose grade point average is below 3.0 may submit an official copy of his/her GRE to support his or her application. 3.Two letters of recommendation from individuals who can comment on the applicant’s academic abilities and potential to succeed in an academically rigorous graduate program. At least one of these letters must be from an instructor who has taught and evaluated the applicant in an academic setting. 4.Completion of the following undergraduate mathematics courses or equivalent:1.MTH 1008 - Matrix Methods 2.MTH 1009 - Calculus I 3.MTH 1013 - Probability and Statistics I 4.MTH 1014 - Probability and Statistics II
Months of entry
Graduates of the MS in Data Mining and Predictive Analytics program will obtain a variety of skills required to analyze large datasets and to develop modeling solutions to support decision making. They will also develop a specialization in either marketing analytics or healthcare analytics. The specialization in healthcare analytics builds upon our division’s undergraduate healthcare informatics major which is now in its fourth year. The program aims to prepare students with the required qualifications to become "data mining analysts/engineers" or "predictive modelers".
Qualification and course duration
Course contact details
- Robert Medrano