Big Data Analytics (PhD)

Big Data Analytics will train researchers with a statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

The program will provide a strong foundation in the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations. The scope of this program is specialized to prepare data scientists and data analysts who will work with very large data sets using both conventional and newly developed statistical methods.

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 42 credit hours of courses, 15 credit hours of restricted elective coursework, and 15 credit hours of dissertation research.

Students must have the following background and courses completed before applying to the Big Data Analytics PhD 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, COP 3503C - Computer Science II. These pre-required courses are basic undergraduate courses from the Math and Computer Science departments. Students without background in COP 3503C can still apply for admission but they will need to take that course sometime after admission in the PhD program. COP 3503C serves as pre-requisite for COP 5711, which is required for the qualifying exam.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

Required Courses—42 Credit Hours

• STA 5104 - Advanced Computer Processing of Statistical Data 3 Credit Hours
• STA 5703 - Data Mining Methodology I 3 Credit Hours
• STA 6106 - Statistical Computing I 3 Credit Hours
• STA 6236 - Regression Analysis 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 6329 - Statistical Applications of Matrix Algebra 3 Credit Hours
• STA 6704 - Data Mining Methodology II 3 Credit Hours
• STA 7722 - Statistical Learning Theory 3 Credit Hours
• STA 7734 - Statistical Asymptotic Theory in Big Data 3 Credit Hours
• STA 6714 - Data Preparation 3 Credit Hours
• CNT 5805 - Network Science 3 Credit Hours
• COP 5711 - Parallel and Distributed Database Systems 3 Credit Hours

Restricted Electives—15 Credit Hours (at least 9 credit hours must be STA coursework)

Other courses may be included in a Plan of Study with departmental approval.

All Ph.D. 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.

• STA 6107 - Statistical Computing II 3 Credit Hours
• STA 6207 - Response Surface and Mixture Experiments 3 Credit Hours
• STA 6226 - Sampling Theory and Applications 3 Credit Hours
• STA 6237 - Nonlinear Regression 3 Credit Hours
• STA 6246 - Linear Models 3 Credit Hours
• STA 6346 - Advanced Statistical Inference I 3 Credit Hours
• STA 6347 - Advanced Statistical Inference II 3 Credit Hours
• STA 6507 - Nonparametric Statistics 3 Credit Hours
• STA 6662 - Statistical Methods for Industrial Practice 3 Credit Hours
• STA 6705 - Data Mining Methodology III 3 Credit Hours
• STA 6707 - Multivariate Statistical Methods 3 Credit Hours
• STA 6709 - Spatial Statistics 3 Credit Hours
• STA 6857 - Applied Time Series Analysis 3 Credit Hours
• STA 7239 - Dimension Reduction in Regression 3 Credit Hours
• STA 7348 - Bayesian Modeling and Computation 3 Credit Hours
• STA 7719 - Survival Analysis 3 Credit Hours
• STA 7935 - Current Topics in Big Data Analytics 3 Credit Hours
• CAP 5610 - Machine Learning 3 Credit Hours
• CAP 6307 - Text Mining I 3 Credit Hours
• CAP 6315 - Social Media and Network Analysis 3 Credit Hours
• CAP 6318 - Computational Analysis of Social Complexity 3 Credit Hours
• CAP 6737 - Interactive Data Visualization 3 Credit Hours
• COP 5537 - Network Optimization 3 Credit Hours
• COP 6526 - Parallel and Cloud Computation 3 Credit Hours
• COP 6616 - Multicore Programming 3 Credit Hours
• COT 6417 - Algorithms on Strings and Sequences 3 Credit Hours
• COT 6505 - Computational Methods/Analysis I 3 Credit Hours
• ECM 6308 - Current Topics in Parallel Processing 3 Credit Hours
• EEL 5825 - Pattern Recognition and Learning from Big Data 3 Credit Hours
• EEL 6760 - Data Intensive Computing 3 Credit Hours
• ESI 5419 - Engineering Applications of Linear, Nonlinear and Integer Programming 3 Credit Hours
• ESI 6247 - Experimental Design and Taguchi Methods 3 Credit Hours
• ESI 6358 - Decision Analysis 3 Credit Hours
• ESI 6418 - Linear Programming and Extensions 3 Credit Hours
• ESI 6609 - Industrial Engineering Analytics for Healthcare 3 Credit Hours
• ESI 6891 - IEMS Research Methods 3 Credit Hours

Dissertation—15 hours

• STA 7980 - Dissertation Research 15 credit hours

Examinations

After passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.

Qualifying Examination

The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) once a year. The courses required to prepare for the examination are STA 5703, STA 6704, CNT 5805, STA 6326, STA 6327 and COP 5711. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their third year and are expected to have completed the exam by the start of their fourth year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program.

Candidacy Examination

The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program.

Admission to Candidacy

The following are required to be admitted to candidacy and enroll in dissertation hours.

• Completion of all coursework, except for dissertation hours
• Successful completion of the qualifying examination
• Successful completion of the candidacy examination including a written proposal and oral defense
• The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars
• Submittal of an approved program of study

Dissertation

After passing the qualifying exam, the student must select a dissertation adviser. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours.

The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Independent Learning

As will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.

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 application requirements, applicants to this program must provide:
• One official transcript (in a sealed envelope) from each college/university attended.
• A personal statement identifying the area of research interest and a description of the applicant's academic and professional experiences.
• Three letters of recommendation.
• A Bachelor's degree or its equivalent in statistics, data analytics or a related field from a regionally accredited institution or recognized foreign institution.
• The student should have a minimum cumulative GPA of 3.0 for all bachelor's level work completed.
• A competitive score on the combined quantitative and verbal sections of the Graduate Record Examination (GRE) or a competitive GMAT score taken within the last five years prior to admission to the program.
• A current curriculum vitae.

Application Deadlines

Big Data Analytics PhD	*Fall Priority	Fall	Spring	Summer
Domestic Applicants	Jan 15	Jul 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.

Financials

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.

Fellowships

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.

Contact Info

Institution Codes
GRE: 5233
GMAT: RZT-HT-58
TOEFL: 5233
ETS PPI: 5233