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    Oct 20, 2021  
2020-2021 Graduate Catalog 
    
2020-2021 Graduate Catalog [ARCHIVED CATALOG]

Big Data Analytics, Ph.D.


Priority Admission Application Deadlines: http://www.grad.usf.edu/majors

Contact Information

College: Muma College of Business
Department: Dean’s Office

Contact Information: http://www.grad.usf.edu/majors


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 strucutred, semi-structured and unstructured datea, from different sourcds, 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, and one independent study/practicum course. In the practicum course (where students register for an independent study), 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. The total number of credit hours for the electives and practicum course should be at least 7 credit 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.