Analytics and Data Science PhD Curriculum

Data Science PhD Curriculum

The general structure of the KSU Ph.D. program will include three stages:

 

Stage One: Pre-Program Requirements

Successful applicants will have completed:

  • A masters degree in a computational field (e.g., engineering, mathematics, computer science, statistics, economics, finance, etc)

  • Calculus I and II

  • Programming Experience (e.g. SAS, R, SQL, Java, Python)

  • Supervised modeling experience

Stage Two: Coursework

The Ph.D. in Analytics and Data Science requires 78 total credit hours spread over (an expected) four years of study. This includes 48 hours of interdisciplinary course work, 15 hours of internship/project work, and 15 hours of dissertation research. In the first year, students take 18 hours of “core” courses in Computer Science, Mathematics, and Statistics, followed by comprehensive examinations during the summer. In year two, students will take an additional 9 hours of required courses and are encouraged to begin taking additional elective courses (15 credits) that can lead to a “Concentration” area.

Core Required Courses for the Ph.D. in Analytics and Data Science:

First Year Core (18 credits)
Students will take these courses in their first year and will have comprehensive exams during the summer after their first year covering material from these courses.
  • CS 7265 Big Data Analytics (3 cr)
  • CS 7267 Machine Learning (3 cr)
  • MATH 8010 The Theory of Linear Models (3 cr)
  • MATH 8020 Graph Theory (3 cr)
  • STAT 8240 Data Mining (3 cr)
  • STAT 8250 Data Mining II (3 cr)
 
Additional Required Statistics Courses (6 credits)
Students shall take STAT 8330 and 3 additional credits of graduate level STAT courses above the 6000 level in consultation with the program director.
  • STAT 8330 Applied Binary Classification (3 cr)

 Additional Required Mathematics Course (3 credits)

  • MATH 8030 Applied Discrete & Combinatorial Mathematics for Data Analysts (3 cr)
 
Electives and Concentration (15 credits)
Students will take additional graduate courses in DS, IT, IS, STAT, CS, MATH, or other related subject area at or above the 6000 level. These should be selected in consultation with your advisor and the graduate program director, and should be reflected in your plan of study. Some of these electives can be DS 9700 and 9900 once those minimum requirements are filled.
  • DS 9000 Doctoral Seminar in Data Science (1-3 cr)

Stage Three: Project Engagement and Research/Dissertation

In the final two years of the program, students will primarily be working as researchers on projects in one of our research labs in our Center for Statistics and Analytical Research and directly with a Kennesaw State faculty member to develop and conduct their dissertation research. Students will also continue to work with the faculty advisor through their final year of project engagement and dissertation research. 

Internship and Applied Research Projects (15 credits)
To ensure that our Ph.D. students in Analytics and Data Science are exposed to the latest issues and challenges, individuals will engage with one (or more) of the dozens of organizations that sponsor doctorate-level projects. These projects have included health care, banking, retail, government, and consumer finance. Students will take a minimum of 15 and a maximum of 18 hours of DS 9700. This course should only be taken by students who have already completed comprehensive exams or with permission of the program coordinator. 
  • DS 9700 Doctoral Internship (1-6 cr) 
 
Dissertation Research (15 credits)
A Ph.D. in Analytics and Data Science requires a formal Dissertation, supervised by a committee comprised of interdisciplinary Data Science faculty across Kennesaw State. Students will take a minimum of 15 hours of DS 9900 in order to graduate. This course should only be taken by students who have already completed comprehensive exams or with permission of the program coordinator. 
  • DS 9900 Dissertation Research (1-9 cr)

If you have any questions, please check our Frequently Asked Questions (FAQ).

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