Big Data Analytics, Ph.D.

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Muma College of Business
Department: Dean’s Office

Major Contacts, Deadlines, and Delivery Information

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

Core (6 Credit Hours)

COT 6405 Introduction to the Theory of Algorithms Credit Hours: 3
ISM 7936 Design Science Research Seminar Credit Hours: 3

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.

HUMAN PERSPECTIVE:
Ethics and Privacy
GEB 6445 Social, Ethical, and Legal Systems Credit Hours: 3
GEB 6457 Ethics, Law and Sustainable Business Practices Credit Hours: 3
Cognitive Biases and Impact on Modeling, Decision Making
EXP 7099 Graduate Seminar in Experimental Psychology Credit Hours: 1-3
EXP 6608 Cognitive Psychology Credit Hours: 3
Data Communication and Storytelling
ISM 6419 Data Visualization Credit Hours: 3
CIS 6930 Special Topics Credit Hours: 1-5 Taken as Data Visualization (3 Credit Hours)
CAI 5615 Affective Computing Credit Hours: 3
Causality and Experimentation

(Student chooses two courses from this category):
EDF 7474 Applied Multilevel Modeling in Education Credit Hours: 3
ESI 6247 Statistical Design Models Credit Hours: 3
STA 6205 Design of Experiments Credit Hours: 3
INP 6935 Topics in Industrial-Organizational Psychology Credit Hours: 3
PSY 6217 Research Methods and Measurement Credit Hours: 2-4
PHC 6020 Clinical Trials: Design, Conduct, and Analysis Credit Hours: 3
COMPUTATIONAL PERSPECTIVE:
Data Mining
CAI 5135 Data Mining Credit Hours: 3
ISM 6136 Data Mining Credit Hours: 3
ESI 6635 Advanced Analytics I Credit Hours: 3
Machine Learning
ISM 6251 Machine Learning Credit Hours: 3
CIS 6930 Special Topics: Predictive Analytics (3 Credit Hours) (proposed)
CAI 5107 Machine Learning Credit Hours: 3
EIN 6934 Special Industrial Topics I Credit Hours: 1-3
ESI 6681 Deep Learning Analytics Credit Hours: 3
Databases/Big Data
ISM 6218 Advanced Database Management Credit Hours: 3
ISM 6562 Big Data for Business Credit Hours: 3
CIS 6930 Special Topics: Advanced Databases (3 Credit Hours)
CIS 6930 Special Topics: Introduction to Hadoop and Big Data (3 Credit Hours)
Mathematics/Linear Algebra
MAS 5145 Advanced Linear Algebra Credit Hours: 3
STA 6746 Multivariate Analysis Credit Hours: 3
STATISTICAL PERSPECTIVE:

Probability/Statistics
STA 5166 Statistical Methods I Credit Hours: 3
STA 5446 Probability Theory I Credit Hours: 3
STA 5326 Mathematical Statistics I Credit Hours: 3
Optimization
MAP 6205 Control Theory and Optimization Credit Hours: 3
ESI 6491 Linear Programming and Network Optimization Credit Hours: 3
ESI 6448 Integer Programming Credit Hours: 3
EIN 6935 Special Industrial Topics II Credit Hours: 1-3 Taken as: Nonlinear Optimization and Game Theory Credit Hours 3
EIN 6936 Special Industrial Topics III Credit Hours: 1-3 Taken as Multi-Objective Optimization Credit Hours: 3

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.

ISM 6905 Independent Study Credit Hours: 1-6
IDS 6940 Cooperative Internship Credit Hours: 0-6
Other elective graduate course approved by the Graduate Director

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.

ISM 7980 Dissertation Credit Hours: 2-12 (24 credits required for this program)

University of South Florida

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