The Master of Science in Statistics and Data Science, Data Science track focuses on data analytics and its application to business, social, and health problems.
The Data Science track in the Statistics and Data Science MS program focuses on data analytics and its application to business, social, and health problems.
The program is particularly suited for individuals who have completed an undergraduate program in mathematics, statistics, economics, business, or other related fields, and wish to pursue a career in data science. Data scientists analyze massive data sets to uncover trends and associations, and make theoretically sound decisions on, for example, business, social, and health subjects. Data scientists have one of the most coveted jobs, as the demand for them far exceeds the existing number of qualified persons in the area. Currently, the work force in the data science industry consists mainly of individuals trained with post college education. To date, very few university degree programs exist for training students for such a large and growing industry in the United States.
The Data Science track in the Statistics and Data Science MS program is composed of 24 credit hours of required courses and 12 credit hours of restricted electives. Students must also pass a comprehensive written examination.
Students must have the following background and courses completed before applying to the Statistics & Data Science, Data Science track MS program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra. These pre-required courses are basic undergraduate courses from the Math department.
All MS students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.
Total Credit Hours Required: 36 Credit Hours Minimum beyond the Bachelor's Degree
Required Courses: 24 Credit Hours
Note: STA 5703 and STA 6704 both require research projects that fulfill the independent learning requirement for the program.
- STA 5104 - Advanced Computer Processing of Statistical Data 3 Credit Hours
- STA 6714 - Data Preparation 3 Credit Hours
- STA 6238 - Logistic Regression 3 Credit Hours
- STA 6326 - Theoretical Statistics I 3 Credit Hours
- STA 6327 - Theoretical Statistics II 3 Credit Hours
- STA 6236 - Regression Analysis 3 Credit Hours
- STA 5703 - Data Mining Methodology I 3 Credit Hours
- STA 6704 - Data Mining Methodology II 3 Credit Hours
Elective Courses: 12 Credit Hours
Select electives from the following courses. No more than one COP course can be selected.
- COP 4710 - Database Systems 3 Credit Hours
- COP 5711 - Parallel and Distributed Database Systems 3 Credit Hours
- COP 6730 - Transaction Processing 3 Credit Hours
- COP 6731 - Advanced Database Systems 3 Credit Hours
- STA 5205 - Experimental Design 3 Credit Hours
- STA 5505 - Categorical Data Methods 3 Credit Hours
- STA 5825 - Stochastic Processes and Applied Probability Theory 3 Credit Hours
- STA 6106 - Statistical Computing I 3 Credit Hours
- STA 6226 - Sampling Theory and Applications 3 Credit Hours
- STA 6237 - Nonlinear Regression 3 Credit Hours
- STA 6507 - Nonparametric Statistics 3 Credit Hours
- STA 6707 - Multivariate Statistical Methods 3 Credit Hours
- STA 6857 - Applied Time Series Analysis 3 Credit Hours
- STA 6705 - Data Mining Methodology III 3 Credit Hours
- FIN 6406 - Strategic Financial Management 3 Credit Hours
Comprehensive Examination
All students must take a comprehensive written examination covering the five courses STA 6326, STA 6327, STA 5104, STA 6714 and STA 6238. For full-time students, this examination will normally be taken just prior to the start of the second year of their graduate work. Students are allowed two attempts to pass the exam. Failure to pass after the second attempt will result in dismissal from the program.
Independent Learning
- STA 5703 and STA 6704 both require research projects that fulfill the independent learning requirement for the program. Both courses require students to build models for target variables of projects with very large sets of data, write a report, and then give an oral presentation on their independent learning experiences.
Application Requirements
For information on general UCF graduate admissions requirements that apply to all prospective students, please visit the Admissions section of the Graduate Catalog. Applicants must apply online. All requested materials must be submitted by the established deadline.
In addition to the general UCF graduate admission requirements, applicants to this program must provide:
- One official transcript (in a sealed envelope) from each college/university attended.
- Official, competitive GRE or GMAT score taken within the last five years.
- Résumé.
Applicants not qualified for regular graduate status may be initially admitted to the university in non-degree-seeking status and later admitted to regular status once all deficiencies have been eliminated, although only nine hours of graduate course work taken as a non-degree-seeking student can count toward a graduate degree.
Meeting minimum UCF admission criteria does not guarantee program admission. Final admission is based on evaluation of the applicant's abilities, past performance, recommendations, match of this program and faculty expertise to the applicant's career/academic goals, and the applicant's potential for completing the degree.
Application Deadlines
| Data Science | *Fall Priority | Fall | Spring | Summer |
| Domestic Applicants | Jan 15 | Jul 1 | Dec 1 | Apr 1 |
| International Applicants | Jan 15 | Jan 15 | | |
| *Applicants who plan to enroll full time in a degree program and who wish to be considered for university fellowships or assistantships should apply by the Fall Priority date. |
Contact Info
GRE: 5233
GMAT: RZT-HT-58
TOEFL: 5233
ETS PPI: 5233
