The Data Science B.S. spans a variety of sub-disciplines within the greater field of data science, including mathematics, statistics, specialized programming, algorithm design, and the fundamentals of uncovering, interpreting and communicating the business insights obtained from the data to help businesses make informed decisions.
This is an interdisciplinary Bachelor of Science program in Data Sciences, offered jointly by the departments of Computer Science, Statistics and Data Science, Mathematics, and Industrial Engineering and Managements Systems at UCF.
This program will entail 120 credit hours for graduation, with 49 credit hours of required courses. By graduation, students will be able 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. - Implement algorithms for data mining and analysis and explain their time- and space-efficiency.
- Perform data acquisition and management for large and dynamic databases.
- Present and communicate knowledge derived from data in an unambiguous and convincing
manner.
The curriculum includes 49 hours of required courses that ensure students have skills in algorithms and statistical techniques for extracting information, including:
- Computer Science I, which introduce students to algorithms and algorithm analysis for efficient
computation - Programming for Scientists, which introduces students to computational data analysis using
languages such as Python and R - Statistical Methods I and II, which introduces students to the statistical fundamentals of data
analytics - Fundamentals of Data Science, which introduces techniques for collecting, analyzing, and
processing and generating big data sets with a parallel, distributed algorithm on a cluster - Praxis in Data Science, which teaches students how to use standard tools for data analysis
including data visualization and includes an applied learning component via a research project or
intern experience.
Required Courses
Core Requirements: Advanced Level (49 Credit Hours)
Complete all of the following courses:
- COP 3502C – Computer Science I Credit Hours: 3
- COP 4283 – Programming for Scientists Credit Hours: 3
- ISC 4241 – Data Science I Credit Hours: 3
- ISC 4242 – Data Science II Credit Hours: 3
- ISC 4301 – Predictive Analytics Credit Hours: 3
- ISC 4401 – Data Management Technology Credit Hours: 3
- ISC 4501 – Data Graphics and Visualization Credit Hours: 3
- ISC 4701 – Praxis in Data Analysis Credit Hours: 3
- MAS 3105 – Matrix and Linear Algebra Credit Hours: 4
- STA 4038 – Statistical Foundations of Data Science
and Artificial Intelligence I Credit Hours: 3 - STA 4039 – Statistical Foundations of Data Science
and Artificial Intelligence II Credit Hours: 3 - STA 4163 – Statistical Methods II Credit Hours: 3
- STA 4164 – Statistical Methods III Credit Hours: 3
- STA 4724 – Big Data Analysis Methods Credit Hours: 3
Select One:
- COT 3100C – Introduction to Discrete Structures Credit Hours: 3
- MHF 3302 – Logic and Proof in Mathematics Credit Hours: 3
Select One:
- CAP 4670 Algorithms for Machine Learning Credit Hours: 3
- ESI 4312 Deterministic Methods for Operations Research Credit Hours: 3
- MAP 4447 Mathematical Aspects of Machine Learning and
Artificial Intelligence Credit Hours: 3 - STA 4241 Statistical Learning Credit Hours: 3
Program Director
