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2025-2026 Graduate Catalog
Big Data Analytics, Ph.D.
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 Return to: Graduate Majors (A-Z)
The Ph.D. in Big Data Analytics is an interdisciplinary area of scientific methods, processes and systems to extract knowledge and insight from large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes. This interdisciplinary major comprises faculty from Artificial Intelligence, Computer Science, Engineering, Business, Arts and Sciences, Public Health, and other areas. Students in the program will develop broad theoretical and applied skills, including how to design, implement, and evaluate information-focused big data technologies that support decision-making across social and organizational contexts.
Major Research Areas:
Big Data, Data Analytics, Data Mining, Database Management, Statistical Computing, Ethics and Human Factors, Artificial Intelligence, Machine Learning, Data Science, Experiment Design
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Admission Information
Must meet University Admission and English Proficiency requirements as well as requirements for admission to the major, listed below. - Three letters of recommendation
- Personal statement of purpose/interest
- Resume/CV
- PDF of unofficial or official transcripts
- PDF of English proficiency, if needed
- Master’s degree in a relevant area preferred
- Prior training and/or experience in technology, including areas such as computer programming through data structures, database management systems, linear algebra, and networking and graph theory. Each student will be reviewed to determine their level of technical qualifications to pursue the Ph.D. If deficiencies are noted, additional suggested coursework may be required for admission.
- The GRE is suggested but not required. Applicants may provide a PDF of unofficial GRE scores.
All applications will be reviewed by an interdisciplinary Doctoral Program Committee that will be charged with making recommendations for admissions. This committee will also, as applicable, recommend applications for consideration for financial aid or assistantships that are available. Foundation Courses Students are expected to have completed coursework in the foundation areas of data structures, linear algebra and graph theory prior to entering the program. Curriculum Requirements
Total Minimum Hours - 72 hours post-bachelor’s - Core - 6 Credit Hours
- Human Issues Courses - 15 Credit Hours
- Computational Factors Courses - 12 Credit Hours
- Mathematical/Statistical Courses - 9 Credidt Hours
- Electives - 3 Credit Hours
- Practicum/Independent Study - 3 Credit Hours Minimum
- Dissertation - 24 Credit Hours Minimum
Course Requirements:
The curriculum is divided into three different areas (Human Issues, Computational Factors, and Mathematical and Statistical Processes) from which students are required to gain competency. Students must take at least one course (or two if specified) from each of the categories listed below each area. Human Issues Courses (15 Credit Hours)
Ethics and Privacy (3 Credit Hours)
Select one: Cognitive Biases Impact on Modeling, Decision Making (3 Credit Hours)
Select one: Data Communication and Storytelling (3 Credit Hours)
Select one: Causality and Experimentation (6 Credit Hours)
Select two: Computational Factors (12 Credit Hours)
Artificial Intelligence and Deep Learning (6 Credit Hours)
Machine Learning, Data Mining and Big Data (6 Credit Hours)
Select two: Mathematical and Statistical Processes (9 Credit Hours)
Mathematics (3 Credit Hours)
Select one: Statistics (3 Credit Hours)
Select one: Optimization (3 Credit Hours)
Select one: Electives (3 Credit Hours Minimum)
Students are expected to take at least one three (3) credit hour elective course chosen in consultation with the Graduate Director. The elective course can be in the area of the student’s specialization from one of the three perspective areas, or outside in consultation and approval by the Major Professor and the Graduate Director. Practicum/Independent Study (3 Credit Hours Minimum)
Students must complete either three (3) credit hours of Practicum or Independent Study course, depending on the project. In the practicum course, students will solve a real-world big data analytics project. This real-world big data analytics project could be done jointly with an industry partner as part of an internship. In the independent study course, students will solve a real-world big data analytics project completed inside the University, in the form of a faculty-supervised project versus an industry internship. Comprehensive Qualifying Exam
Students must pass a comprehensive written and oral examination. The exam will be based on a completed research paper and accompanying code written by the student on a big data analytics project. Dissertation (24 Credit Hours Minimum)
After admission to candidacy, a doctoral candidate must write and then defend a dissertation as the final phase of the doctoral program. Refer to department handbook for more information. Students enroll in one of the dissertation courses confirmed by the advisor. The student’s progress in the program is monitored by a supervisory doctoral committee, typically appointed early in the student’s major. This committee consists of at least five members, at least one of whom are from outside Bellini College. The Major Professor or a Co-major Professor can be from another college. Exit Survey
All students are required to complete the college exit survey. |
Return to: Graduate Majors (A-Z)
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