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)
    • Supervised modeling experience

    Because much of the Statistics material utilizes Base SAS Programming, applicants are encouraged to have a Base SAS Certification.

  • The Ph.D. in Analytics and Data Science will begin with 48 hours of core 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 (24 Credit Hours)

    • STAT 8020 - Advanced Programming in SAS
    • STAT 8240 - Data Mining
    • STAT 8250 - Data Mining II
    • STAT 8260 - Segmentation Models
    • STAT 8330 - Applied Binary Classification

    Select three from the following:

    • STAT 7900 - Special Topics
    • STAT 8110 - Quality Control and Process Improvement
    • STAT 8140 - Six Sigma Problem Solving
    • STAT 8340 - Social Network Analysis
    • STAT 8370 - Applied Affinity Analysis
    • STAT 8380 - Churn Modeling
    • STAT 8399 - Design and Analysis of Massive Survey Data

    Mathematics Core (9 Credit Hours)

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

    Computer Science Core (15 Credit Hours)

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

    Plus two Computer Science electives at or above the 7000 level.

    Additional Required Courses

    • 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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