Yes, the program will be delivered by face to face instruction on the main campus of UCF.
Related FAQs
Students are expected to finish the program in 20 months following the cohort model. Special accommodations can be made for a student that can not continue in a given cohort to joint the next cohort. However, our program has been designed to be completed in 20 months and students will be taught in cohorts starting a year apart. As a result, a student that drops from a cohort may need to wait a year before having an available next cohort to join.
Students will enroll in cohorts that start each fall semester. Each semester they take two courses; thus all students are part-time students and it will not be possible to take courses full-time in the program. The students would all take the first 6 required courses in the first year of the program. The second year would be devoted to taking one more required course, two electives and the project course.
Students will take two courses per academic semester of 3 credit hours each. As a result, the workload will be equivalent to a part-time student. This program is designed to be highly demanding but appropriate for working professionals.
The program will be delivered by face to face instruction on the main campus of UCF in classes scheduled after 5:00pm or on weekends.
The plan of study is precisely the ten course series listed on the Program website.
The program is a 30 credit hour interdisciplinary program that prepares students to develop algorithms and computerized systems to facilitate the discovery of information from large amounts of data. It will utilize the technical aspects of big data analytics, including algorithm design, programming, acquisition, management, mining, analysis, and interpretation of data.
The students will learn to:
- Use state-of-the-art software tools to perform data mining and analysis on large structured and unstructured data sets, and transform such data into knowledge.
- Design and implement new algorithms for data mining and analysis, and study their time-, space-, and energy-efficiency.
- Perform data acquisition and management for extremely large and dynamic databases.
- Present and communicate knowledge derived from data in an unambiguous and convincing manner.
The course of study is as follows:
Core Courses:
- Statistical Analysis (STA 5206)
- Parallel and Distributed Database Systems (COP 5711)
- Machine Learning (CAP 5610)
- Text Mining I (CAP 6307)
- Network Science (COT 6938)
- Data Mining Methodology I (STA 5703)
- Data Mining Methodology II (STA 6704)
- Project in Data Analytics (CAP 6942)
Electives (must choose 2):
- Parallel and Cloud Computation (COP 6526)
- Social Media and Network Analysis (CAP 6315)
- Computational Analysis of Social Complexity (CAP 6318)
- Interactive Data Visualization (CAP 6737)
- Data Preparation (STA 6714)
- Machine Learning Methods for Biomedical Data (CAP 6545)