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
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.
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.