Big Data Analytics (PhD) - Statistics Track

Program Overview

The interdisciplinary program combines statistics, computer science, and industry collaborations, providing students with a comprehensive foundation in statistical theory, computing, and data mining methodologies. Graduates are well-prepared for careers in data analysis and research in various fields, including business, social sciences, and healthcare.

Program Outline

The program provides a strong foundation in statistical theory and the major methodologies associated with Big Data Analytics, including:

• Predictive analytics
• Data mining
• Statistical analysis
The program has an interdisciplinary component that combines the strengths of statistics and computer science. It focuses on statistical theory, statistical computing, statistical data mining, and their application to business, social, and health problems. The program also features ongoing industrial collaborations.

Outline:

The Ph.D. in Big Data Analytics, Statistics track, requires 72 hours beyond an earned Bachelor's degree. The program is structured as follows:

• Required Courses (42 credit hours):
• STA6106 - Statistical Computing I (3)
• STA6107 - Statistical Computing II (3)
• STA6224 - Bayesian Survey Methods (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA5205 - Experimental Design (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)
• STA6329 - Statistical Applications of Matrix Algebra (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• STA7719 - Survival Analysis (3)
• STA6857 - Applied Time Series Analysis (3)
• STA6236 - Regression Analysis (3)
• STA6246 - Linear Models (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• Elective Courses (15 credit hours):
• Students must complete at least 5 of the following:
• STA7348 - Bayesian Modeling and Computation (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• MAP6465 - Wavelets and Their Applications (3)
• STA7722 - Statistical Learning Theory (3)
• STA6704 - Data Mining Methodology II (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6226 - Sampling Theory and Applications (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7348 - Bayesian Modeling and Computation (3)
• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA6709 - Spatial Statistics (3)
• STA6238 - Logistic Regression (3)
• STA6714 - Data Preparation (3)
• MAA6238 - Measure and Probability I (3)
• STA6705 - Data Mining Methodology III (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• MAS5145 - Advanced Linear Algebra and Matrix Theory (3)
• MAP6207 - Optimization Theory (3)
• MAA7239 - Asymptotic Methods in Mathematical Statistics (3)
• Other courses may be included in a Plan of Study with departmental approval.
• Dissertation (15 credit hours):
• Students must select a dissertation advisor by the end of the first year.
• In consultation with the dissertation advisor, the student should form a dissertation advisory committee.
• The dissertation advisor 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 taking the candidacy exam and 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 21 hours of dissertation research credit.

Assessment:

The program includes several examinations:

• Qualifying Examination:
• A written examination 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 6326, STA 6327, STA 6236, STA 6246, STA 6366, and STA 6346.
• Students must obtain permission from the Graduate Program Coordinator to take the examination.
• Students normally take this exam just before the start of their second year and are expected to have completed the exam by the start of their third 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.
• Candidacy Examination:
• 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.
• Dissertation Defense:
• Upon completion of a student's research, the student's committee schedules an oral defense of the dissertation.
• Most students complete the program within five years after obtaining their bachelor's degree.
• Students are expected to complete the dissertation in no more than seven years from the date of admission to the program.
• The dissertation defense examination can be taken no more than two times.

Other:

• 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.
• PhD Students can obtain their Master's degree in Statistics & Data Science - Statistics Track along the way to their PhD degree in Big Data Analytics – Statistics track.
• To satisfy the requirements for the MS degree, the student must complete the requirement for the MS degree.
• The student has the option of choosing between thesis option or non-thesis option.
• As with all graduate programs, independent learning is an important component of the Big Data Analytics – Statistics track doctoral program.
• Students will demonstrate independent learning through research seminars and projects and the dissertation.