Curriculum

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

Pre-Program Requirements

Coursework

Project Engagement and Research/Dissertation

  • 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
  • The Ph.D. in Analytics and Data Science will begin with 48 hours of course work/instruction, spread over (expected) four years of study, plus six hours of electives and 24 (minimum) hours of dissertation and internship (78 total hours). In response to market needs and skill gaps, the Ph.D. in Analytics and Data Science will have a strong interdisciplinary and application orientation.

    Students will be required to complete a comprehensive examination of their course materials before they are considered to have completed this stage. The comprehensive examination will cover materials from all of the three areas of study; Computer Science, Mathematics and Statistics.

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

    Statistics Core 

    • STAT 8240 - Data Mining
    • STAT 8250 - Data Mining II
    • STAT 8330 - Applied Binary Classification

    Select electives from the following:

    • STAT 7900 - Special Topics
    • STAT 8110 - Quality Control and Process Improvement
    • STAT 8140 - Six Sigma Problem Solving
    • STAT 8030 - R Programming
    • STAT 8210 - Applied Regression Analysis
    • STAT 8220 - Time Series Forecasting
    • STAT 8225 - Applied Longitudinal Data Analysis
    • STAT 8310 - Applied Categorical Data Analysis
    • STAT 8320 - Applied Multivariate Data Analysis

    Mathematics Core 

    • MATH 8010 - The Theory of Linear Models
    • MATH 8020 - Graph Theory
    • MATH 8030 - Applied Discrete & Combinatorial Mathematics for Data Analysts

    Computer Science Core 

    • CS 7263 - Text Mining
    • CS 7267 - Machine Learning
    • CS 7265 - Big Data Analytics
    • CS 7260 - Advanced Data Base Systems

    Additional Required Courses

    • DS 9000 - Doctoral Research Seminar
    • DS 9700 - Doctoral Internship
    • DS 9900 - Ph.D. Dissertation Research
    • Two free electives, to be selected from the Statistics, Mathematics, or Computer Science content area.

    Program Total (78 Credit Hours)

  • The Ph.D. in Analytics and Data Science is an advanced degree with a dual focus of application and research - where students will engage in real world business problems, which will inform and guide their research interests.

    To ensure that our Ph.D. students in Analytics and Data Science are exposed to the latest issues and challenges of working across a wide variety of data contexts, individuals will be required to engage with one (or more) of the dozens of organizations which have agreed to sponsor doctorate-level projects for a minimum of three semesters (9 credit hours of engagement + 15 credit hours of dissertation research). These organizations span the continuum of application domains, including health care, banking, retail, government, and consumer finance. Students will also continue to work with the faculty adviser through their final year of project engagement and dissertation research.

    A Ph.D. in Analytics and Data Science will require a formal Dissertation process, involving an interdisciplinary committee, comprised of faculty from Statistics, Computer Science, and Mathematics.

Students pursuing a Ph.D. in Analytics and Data Science would be required to take 48 course hours, 6 hours of electives spread over four years, dissertation research (12 hour minimum) and internship (12 hour minimum). In total, this degree is a minimum of 78 credit hours of courses, internship and dissertation.

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