The Master of Science in Statistics and Data Science provides a sound foundation in statistical theory, statistical methods, numerical methods in statistics, and the application of computer methodology to statistical analyses. The MS is particularly suited for individuals who have completed an undergraduate program in mathematics, statistics, or computer science, but is also available to those from other disciplines who wish to develop an expertise in statistics and data science.

The Statistics and Data Science MS program requires a minimum of 36 credit hours beyond the bachelor's degree. The degree in Statistics and Data Science includes 21 credit hours of required courses, 9 credit hours of restricted electives, and passing an oral defense of thesis or completing a research project and an additional elective.

Students must maintain a minimum GPA of 3.0, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.

This degree has 1 track: Data Science Track. For further details on the Data Science Track, please see the Statistics and Data Science MS, Data Science Track Catalog at the bottom of this page for further details on this Track.

**Total Credit Hours Required: 36 Credit Hours Minimum beyond the Bachelor's Degree**

## Program Prerequisites

Students must have the following background and courses completed before applying to the Statistics & Data Science 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.

## Degree Requirements

0 Total Credits

- 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.

### Required Courses

21 Total Credits

- Complete all of the following
- Complete the following:
- STA5205 - Experimental Design (3)
- STA6106 - Statistical Computing I (3)
- STA6236 - Regression Analysis (3)
- STA6326 - Theoretical Statistics I (3)
- STA6327 - Theoretical Statistics II (3)
- STA6329 - Statistical Applications of Matrix Algebra (3)

- Note: STA 6106 provides an independent learning experience for the program. It requires a research project that results in a written report or oral presentation.
- Complete at least 1 of the following:
- STA6246 - Linear Models (3)
- STA6707 - Multivariate Statistical Methods (3)

### Elective Courses

9 Total Credits

- Complete all of the following
- Earn at least 9 credits from the following:
- STA5505 - Categorical Data Methods (3)
- STA5825 - Stochastic Processes and Applied Probability Theory (3)
- STA6107 - Statistical Computing II (3)
- STA6226 - Sampling Theory and Applications (3)
- STA6224 - Bayesian Survey Methods (3)
- STA6237 - Nonlinear Regression (3)
- STA6346 - Advanced Statistical Inference I (3)
- STA6347 - Advanced Statistical Inference II (3)
- STA6507 - Nonparametric Statistics (3)
- STA6662 - Statistical Methods for Industrial Practice (3)
- STA6709 - Spatial Statistics (3)
- STA6857 - Applied Time Series Analysis (3)
- STA5104 - Advanced Computer Processing of Statistical Data (3)
- STA5703 - Data Mining Methodology I (3)
- STA6704 - Data Mining Methodology II (3)
- STA6705 - Data Mining Methodology III (3)
- STA6714 - Data Preparation (3)
- COP5711 - Parallel and Distributed Database Systems (3)
- COP6730 - Transaction Processing (3)
- COP6731 - Advanced Database Systems (3)
- STA6238 - Logistic Regression (3)
- STA7722 - Statistical Learning Theory (3)
- STA7734 - Statistical Asymptotic Theory in Big Data (3)
- STA5738 - Data and Analytical Methodology for Metropolitan and Regional Areas (3)
- STA6223 - Conventional Survey Methods (3)
- STA7239 - Dimension Reduction in Regression (3)
- STA7348 - Bayesian Modeling and Computation (3)
- STA7719 - Survival Analysis (3)
- STA7935 - Current Topics in Big Data Analytics (3)
- CNT5805 - Network Science (3)
- STA5176 - Introduction to Biostatistics

- 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 research project. Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.

### Thesis/Nonthesis Option

6 Total Credits

- Complete 1 of the following
Thesis Option- Complete all of the following
- For this option, the MS degree requires a total of at least 36 credit hours comprised of at least 30 credit hours of course work and 6 credit hours of thesis. This includes the 21 credit hours of the core courses, 9 credit hours of `Electiveâ€™ courses, and 3-6 credit hours of a two-course sequence. No more than 6 credit hours of independent study or directed research may be credited toward the degree. It is strongly recommended that the student select a thesis adviser and establish a program of study by the completion of the core courses. With the help of a thesis adviser, the student will form a thesis committee of three members, of which at least two must be from the Department of Statistics. An oral defense of the thesis is required.
- Earn at least 6 credits from the following:
- STA6971 - Thesis (1 - 99)

Nonthesis Option- Complete all of the following
- Nonthesis students will take an additional 3 credit hours of electives and 3 credit hours of independent study for a research project. The electives should be chosen in consultation with the graduate program director. This will consist of 21 credit hours of the core courses, and 12 credit hours of elective courses, and 3 credit hours of independent study for a research project. It is strongly recommended that the student contacts the academic adviser, Graduate Coordinator, and establish a program of study by the completion of the core courses. In addition, students in the nonthesis option are required to complete the research project based on the core courses. The student will choose a research advisor and write a research project report under that person's advice. An oral presentation of their research project is required.
- Earn at least 3 credits from the following types of courses: Courses listed in "Elective courses" above.
- Earn at least 3 credits from the following:
- STA6908 - Directed Independent Studies (1 - 99)

#### Grand Total Credits: **36**

## Application Requirements

## Application Deadlines

## 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.