Pathway to the Data Science Ph.D. Degree

The following outlines the degree requirements for the Data Science Ph.D. program. We estimate that students will need at least four years to complete the program. Course descriptions are available on the Data Science Ph.D. courses page.

Schedule may be subject to change.

Fall Semester, Year One

  • MSDS 510 Computer Programming Foundations for Data Science (3 credit hours)
  • MSDS 525 Data Management Foundations for Data Science (3 credit hours)
  • MSDS 710 Mathematical and Statistical Theory (3 credit hours)

Spring Semester, Year One

  • MSDS 535 Further Mainstream Programming Languages for Data Science (3 credit hours)
  • MSDS 540 Intro to Artificial Intelligence (3 credit hours)
  • MSDS 700 Fundamentals of Database Management Systems (3 credit hours)

Summer Semester, Year One

  • MSDS 720 Advanced Statistics (3 credit hours)
  • MSDS 555 Big Data Management and Analytics (3 credit hours)

Fall Semester, Year Two

  • MSDS 550 Computational Machine Learning (3 credit hours)
  • MSDS 740 Big Data Privacy and Security (3 credit hours)
  • MSDS 725 Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization (3 credit hours)

Spring Semester, Year Two

  • MSDS 580 Research Methods (3 credit hours)
  • MSDS 715 Data modeling for Big Data (3 credit hours)
  • MSDS 565 Predictive Modeling and Analytics (3 credit hours)

Summer Semester, Year Two

  • MSDS 800 Candidacy Exam (3 credit hours)
  • The Candidacy Exam includes six exams, with each covering content from one of six previously taken Foundation and Core courses.

Fall Semester, Year Three

  • MSDS 570 Visual Analytics (3 credit hours)
  • MSDS 730 Deep Learning (3 credit hours)
  • Special Topics and Electives course 1*

*View course options and descriptions

Spring Semester, Year Three

  • MSDS 736 Ethical, Legal and Societal Issues in Big Data Analytics (3 credit hours)
  • Special Topics and Electives course 2*
  • Research Seminar (3 hours)**

**View course options and descriptions

Summer Semester, Year Three

  • Research Seminar (3 hours)**
  • Proposal Defense

Fall Semester, Year Four

Dissertation

Spring Semester, Year Four

  • Dissertation
  • Dissertation Defense

* Special Topics and Electives courses (6 hours)

  • MSDS 560 Natural Language Processing (3 credit hours)
  • MSDS 610 Network and Graph Theory for Data Science (3 credit hours)
  • MSDS 620 Signal Processing for Big Data (3 credit hours)
  • MSDS 655 AI in Cyber Security (3 credit hours)
  • MSDS 727 Digital Image Processing and Understanding (3 credit hours)

** Research Seminar courses (6 hours, at least 1 hour of each course)

  • MSDS 750V Individual Studies (1-3 credit hours)
  • MSDS 870V Literature Review (1-3 credit hours)
  • MSDS 880V Seminar (1-3 credit hours)

Dissertation and Defense (12 hours)

  • MSDS 890 Dissertation and Defense (12 credit hours)