Jul 23, 2024  
2024-2025 Graduate Catalog 
2024-2025 Graduate Catalog

Big Data Analytics, Ph.D.

Muma College of Business  
Department: Dean’s Office 


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 facutly from Arts & Sciences, Business, Engineering, and Public Health.  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

Admission Information

Must meet University Admission and English Proficiency requirements as well as requirements for admission to the major, listed below.
  • Bachelor’s required; 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.
  • GRE scores are to be strong and competitive and will be reviewed holistically in the context of the overall application package
  • Personal statement of purpose/interest
  • 3 Letters of recommendation
  • Current curriculum vitae
  • Virtual interviews
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.  Students who have not completed some of all of these foundation courses need to demonstrate proficiency in these areas by either completing related coursework at USF such as:
        COP 4530        Data Structures
        MAS 3105        Linear Algebra
        MAD 4301       Introduction to Graph Theory
Or equivalent (such as a Course or Certificate) pre-approved by the Graduate Director before registration in the program’s core courses.

Curriculum Requirements

Total Minimum Hours - 72 hours post-bachelor’s

  • Core - 6 Credit Hours
  • Additional Required Coursework -35 Credit Hours Minimum
  • Electives and Practicum - 7 Credit Hours Minimum
  • Dissertation - 24 Credit Hours Minimum

Additional Required Coursework (35 credit hours minimum)

The curriculum is divided into three different perspective areas from which students are required to gain competency. Students must take at least one course from each of the 11 categories listed below each perspective and an additional course from the Causality and Experimentation category.  


Electives and Practicum (7 Credit Hours Minimum)

Students are expected to take at least one elective course chosen in consultation with the Graduate Director, and either independent study or practicum 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.

The total combined number of credit hours for the electives and independent study/practicum course should be at least 7 hours.

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.