The Mathematical Science Graduate Certificate on Data Modeling is designed for students to gain knowledge of mathematical modeling and analytics techniques for data extraction, and to prepare their career on data analyst and data architect.
The mathematical science graduate certificate on data modeling requires taking six graduate courses related to dat modeling.
Total Credit Hours Required: 18 Credit Hours Minimum beyond the Bachelor's Degree
Program Prerequisites
This is a rigorous postgraduate program in mathematics, and as such, all required courses in the program are proofbased, emphasizing logical reasoning and the formulation and validation of mathematical arguments. The program is designed to build on a strong mathematical foundation, ensuring students are well-prepared for advanced study and professional applications of mathematics.
To be eligible for admission to the graduate certificate program, students must demonstrate proficiency in foundational undergraduate mathematics. This includes mastery of the calculus sequence, differential equations, and linear algebra. In addition, applicants are expected to have familiarity with the principles and techniques of mathematical proof. This prerequisite knowledge should be equivalent to UCF’s MHF 3302: Logic and Proof in Mathematics, and, ideally, students should have completed MAA 4226: Advanced Calculus I or a comparable course to strengthen their readiness for the program's proof-intensive curriculum.
Degree Requirements
Required Courses
6 Total Credits
- Complete the following:
- MAS5145 - Advanced Linear Algebra and Matrix Theory (3)
- MAA5237 - Mathematical Analysis (3)
Elective Courses
12 Total Credits
- Complete at least 4 of the following:
- MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)
- MAT5712 - Scientific Computing (3)
- MAP5336 - Ordinary Differential Equations and Applications (3)
- MAP6356 - Partial Differential Equations (3)
- MAA5238 - Measure and Probability I (3)
- MAP6111 - Mathematical Statistics (3)
- MAP6197 - Mathematical Introduction to Deep Learning (3)
- MAP6469 - Bayesian Analysis and Approximation Theory (3)
- MAP6385 - Applied Numerical Mathematics (3)
Grand Total Credits: 18
Application Requirements
Application Deadlines
