The Master of Science in Data Analytics program provides students with the ability to develop algorithms and computer programs for discovery of information from large amounts of data. This includes the architecture of programs, as well as technical details of algorithm development. Students are expected to be able to write and maintain novel computer programs that make efficient use of cutting-edge computer technology.
Students in this nonthesis program receive a broad background in the areas of parallel programming, machine learning, data mining, and network science while specializing in particular areas of data analytics practice. Students successfully completing this program will have exhibited breadth as well as depth of capability involving discovery of knowledge from "big data."
The MS in Data Analytics requires 30 credit hours and includes a project, which is a culminating experience. Students must receive a grade of "B" or higher in all courses.
Total Credit Hours Required: 30 Credit Hours Minimum beyond the Bachelor's Degree
An undergraduate degree in computer science, statistics, computer engineering or information technology is desirable but not required. Applicants without a strong undergraduate background in computer science or statistics must demonstrate an understanding of the material covered in the following upper division undergraduate courses:
- COP 3330 Object-Oriented Programming
- COP 3503C Computer Science II
- COP 4710 Database Systems
- STA 2023 Statistical Methods I
- Programming experience or STA 4164 Statistical Methods III
24 Total Credits
- Complete the following:
- CAP5610 - Machine Learning (3)
- CNT5805 - Network Science (3)
- COP5711 - Parallel and Distributed Database Systems (3)
- COP6526 - Parallel and Cloud Computation (3)
- STA5206 - Statistical Analysis (3)
- STA5703 - Data Mining Methodology I (3)
- STA6704 - Data Mining Methodology II (3)
- CAP6942 - Project in Data Analytics (3)
Restricted Elective Courses
6 Total Credits
- Complete at least 2 of the following:
- CAP6307 - Text Mining I (3)
- CAP6315 - Social Media and Network Analysis (3)
- CAP6318 - Computational Analysis of Social Complexity (3)
- CAP6545 - Machine Learning Methods for Biomedical Data (3)
- CAP6737 - Interactive Data Visualization (3)
- STA6714 - Data Preparation (3)
Grand Total Credits: 30
Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.
Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.