Curriculum

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

 academic stages

Stage 1: Pre-Program Requirements

Successful applicants will have completed:

  • Calculus I and II
  • Programming Experience (e.g. SAS, R, SQL, C++, Java)
  • Supervised modeling experience

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

Stage 2: Coursework

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:

  • Prefix

    Course Name

    Credit Hours
  • STAT 8240
    Data Mining I
    3-0-3
  • STAT 8020
    Advanced Programming in SAS
    3-0-3
  • STAT 8330
    Applied Binary Classification
    3-0-3
  • STAT 8250
    Data Mining II
    3-0-3
  • STAT 8260
    Segmentation Models
    3-0-3
  • STAT XXXX
    Statistics Elective
    3-0-3
  • STAT XXXX
    Statistics Elective
    3-0-3
  • STAT XXXX
    Statistics Elective
    3-0-3
  • STAT CORE = 24 HOURS
     
     
  • MATH 8010
    Theory of Linear Models
    3-0-3
  • MATH 8020
    Graph Theory
    3-0-3
  • MATH 8030
    Discrete Mathematics
    3-0-3
  • MATH CORE = 9 HOURS
     
     
  • ACS 7410
    Parallel and Distributed Computing
    3-0-3
  • ACS 7510
    HPC Infrastructure
    3-0-3
  • ACS 7420
    Algorithm Design for Big Data
    3-0-3
  • ACS 8310
    Data Warehousing
    3-0-3
  • ACS XXXX
    ACS Elective
    3-0-3
  • CS CORE = 15 HOURS
     
     
  • TOTAL CORE REQUIREMENTS = 48 HOURS
     
     
  • PROGRAM TOTAL = 78 CREDIT HOURS
     
     

Students pursuing a Ph.D. in Analytics and Data Science would be required to take 48 course hours plus 6 hours of electives spread over four years, plus 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.

Stage 3: Project Engagement and Research/Dissertation

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 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.

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