The Data Analytics MS, Artificial Intelligence (AI) Track is part of the interdisciplinary Master of Science program in Data Analytics, offered jointly by the departments of Computer Science and Statistics. The track emphasizes the technical aspects of artificial intelligence in data analytics, including artificial intelligence algorithm design, advanced object oriented programming, acquisition, management, mining, analysis, and interpretation of data. It is a 30 credit hour program. Students are taught in cohorts by face-to-face/ hybrid synchronous instruction. The program has a required semester-long project, but does not require a thesis. The overall goal is to provide artificial intelligence technical skills to professionals in Florida. While governments want to use artificial intelligence data to improve the life of their citizens, businesses are keen on exploiting AI to better serve their customers. Consequently, there is an increasing demand for Artificial Intelligence practitioners who can create, adapt, and use state-of- the-art tools to obtain insights into automated processes. Usually people with this training have the title of “AI Analyst”, “machine learning engineer”, “computer system engineers”, “network engineers”, and “information security engineer”. Graduates may go on to complete a Ph.D. in Computer Science, Statistics, or a related area and may also seek professional distinction.
Track Prerequisites
For admission, 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 undergraduate courses, by either taking these courses or by convincing the program that the student’s work experience or courses at other institutions have covered this material:
- 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)
The program’s director, assisted by the program’s faculty, will evaluate student applications. At the discretion of the director, prospective students with sufficient industry experience in computer programming will be deemed to have programming experience and the director will decide which of the prerequisites the student may need to make up as a non-degree seeking student (at UCF or elsewhere).
Degree Requirements
Required Courses
30 Total Credits
- Complete the following:
- STA5206 - Statistical Analysis (3)
- CNT5805 - Network Science (3)
- STA5703 - Data Mining Methodology I (3)
- CAP5610 - Machine Learning (3)
- STA6704 - Data Mining Methodology II (3)
- PHI6679 - Digital Ethics (3)
- CAP6614 - Current Topics in Machine Learning (3)
- CAP6640 - Computer Understanding of Natural Language (3)
- CAP5636 - Advanced Artificial Intelligence (3)
- CAP6942 - Project in Data Analytics (3)
Grand Total Credits: 30
Financial Information
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.
Fellowship Information
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.
The MSDA-AI track will be an intense, 5 (or 4) semester long program intended for individuals with STEM heavy majors and some quantitate work experience. The educational goal of the program is to bridge a student’s undergraduate degree with artificial intelligence applications in a practical context, providing fundamental knowledge and in demand skills for the technical marketplace.
PROGRAM HIGHLIGHTS
- Less than 2 years, intensive course schedule. Program can be completed in four or five consecutive academic terms: Fall, Spring & Summer
- Coursework covering the fundamentals of data science, analytics, machine learning, deep learning, and artificial intelligence.
- Applied, integrated real-world capstone project
SAMPLE COURSE SCHEDULE
Semester 1 (Fall)
- STA 5206 Statistical Analysis
Semester 2 (Spring)
- STA 5703 Data Mining Methodology I
- CAP 5610 Machine Learning
Semester 3 (Summer)
- STA 6704 Data Mining Methodology II
Semester 4 (Fall)
- CAP 6614 Current Topics in Machine Learning
- CAP 6640 Computer Understanding of Natural Language
Semester 5 (Spring)
- CAP 5636 Advanced Artificial Intelligence
- CAP 6942 Project in Data Analytics
