The Ph.D. program in Data Science requires 60 credit hours, typically consisting of 48 hours of coursework and 12 dissertation hours. A typical student will complete the program in 4 to 5 years. Coursework consists of the following:

  • 10 core courses (30 hours)
  • Two 3-hour research rotations, to be completed during the summer semesters after years one and two (6 hours)
  • At least 4 elective courses (12 hours)

Students will also complete the following professional activities to qualify for the Ph.D. degree:

  • Attend a data-science seminar series during their first 6 semesters. Seminars are drawn from across the university’s departments and institutes.
  • Participate in SMU Research Day (poster or three-minute thesis competition) at least once
  • Present research at a national or regional conference
  • Have a paper accepted in an appropriate venue, to be decided by the student’s degree committee

10 Core courses (30 hours)

Students take 10 core courses in Computer Science and Statistics, the component areas of data science. The Basic Exam (see below) will be based on the first-year offerings. A student who can demonstrate competence in a core subject from previous training may substitute an elective for the corresponding core course.

Core courses:Blue indicates theory; red indicates methods.

Computer Science (CS)

Statistics & Data Science (SDS)

CS 7330: File Organization & Database Management

SDS 6327: Math Stat I

CS 7324: ML in Python (MLI) ††


CS/OREM 7331: Data Mining

SDS 6328: Math Stat II

CS 7350: Algorithm Engineering

SDS 6336: Methods I

CS 8321 - Machine Learning & Neural Networks (ML II) or

CS/OREM 8331: Advanced Data Mining

SDS 6337: Methods II


*SDS 6345: Statistical Learning Methods


SDS 7329: Data Security & Ethics

Students take either CS 7324 and CS8321 (ML track) or CS/OREM 7331 and CS/OREM 8331 (data mining track).

Research Rotations (6 hours)

Students complete 6 hours (3 hours each for two summers) of research experiences known as rotations. Before beginning a research rotation, students must pass the Basic Exam (see below).

Research Rotations are a distinguishing feature of the SMU Ph.D. program in Data Science. For each rotation students spend a semester as a research assistant on a practical application of data science methods under the supervision of a program faculty member. The rotations allow students to improve their collaborative and communication skills in technical areas outside their own fields; they familiarize students with problems encountered in application development; and they provide an avenue for research collaboration and potential funding streams from units and organizations outside of the Ph.D. program and potentially outside the University. Research rotations change annually. At the end of each research rotation students provide a final report on the experience.

Elective Courses (12 hours)

Elective courses may be selected from Computer Sciences, Operations Research and Engineering Management, Statistics and Data Science, or other appropriate data science offerings from outside these departments. Elective courses may also come from application areas or other areas of students’ interests, such as Business, Economics, the sciences, and Learning Sciences.

Examples of elective courses currently available include:

Table 2a. Data Science elective courses

Computer Science (CS)

Electrical & Computer Engineering (ECE)

Statistics & Data Science (SDS)

Operations Research & Engineering Management (OREM)

7320 – Artificial Intelligence

7322 – Intro to Natural Language Processing

7323 – Mobile Applications for Sensing and Learning

7337 – Information Retrieval & Web Search

7339 – Computer System Security

CS/ECE 7346 – Cloud Computing

7349 – Data & Network Security

7350 – Algorithm Engineering

7359 – Software Security

8320 – Knowledge-Intensive Problem-Solving

8330 – Database Management Systems

8337 – Information Storage & Retrieval

8350 – Algorithms II

8359 – Advanced Software Security

8364 – Statistical Pattern Recognition

7365 – Adaptive Algorithms for Machine Learning

7374 – Digital Image Processing

7375 – Random Processes in Engineering

8371 – Information Theory

ECE 8372/CS 8372 – Cryptography & Data Security

8381 – Quantum Logic & Design

6355 – Applied Multivariate Analysis

6357 – Categorical Data Analysis

6358 – Statistics for High-Throughput Biological Assays

6360 – Statistical Methods in Epidemiology

6363 – Time Series

6380 – Theory of Sampling

6385 – Nonparametric Statistics

6390 – Bayesian Statistics

6391 – Bayesian Hierarchical Modeling

6397 – Statistical Methods in Clinical Trials

7331 – Modeling Incomplete & Longitudinal Data


7357 – Analytics for Decision Support

8360 – Operations Research Models

7377 – Statistical Design & Analysis of Experiments



Mathematics (MATH)


6370 – Parallel Scientific Computing



Table 2b. Examples of Electives for Potential Application Areas


Learning Science

Computational Chemistry (CHEM)

ITOM 6226 – Operations Analytics

ITOM 6220 – Revenue Management

FINA 6216 – Portfolio Theory & Asset Pricing

FINA 6226 – Quantitative Trading Strategies

MKTG 6224 – Research for Marketing Decisions

MNO 6219 – People & Organizational Analytics

EDU 7309-001 – Intro to Learning Science

EDU 7309-002 – Data Modeling & the Learning Sciences

PSYC 6353 – Psychometrics, Test Construction, & Assessment

HGME 6592 – Team Game Production I

HGME 6381 – Game Production I


6144 – Computer-Assisted Drug Design, Fundamentals & Application

7108 – Statistical Molecular Thermodynamics

6343 – Advanced Computational Chemistry

Economics (ECO)

Psychology (PSYC)

6372, 6374, 6375 – Econometrics I, II & III

7321 – Labor Economics

7322 – Development of Human Capital

7376 – Macroeconometrics

7377 Microeconometrics

7378 Topics in Econometrics

6314 – Adult Psychopathology

6317 – Biological & Neuroscientific Bases of Behavior

6334 – Developmental Psychopathology

6359 – Affective & Social Neuroscience


Please note that some of these courses may require prerequisites.

Other requirements

Students must complete the following professional activities to qualify for graduation. Each student will

  • Attend the Data Science seminar, drawn from relevant seminars of university departments and institutes.
  • Participate at least once in SMU Research Day (poster or three-minute thesis); and
  • Present research at a national or regional conference.


Basic Exam. This exam covers the content of the six first-year courses and is taken at the end of the first year in the program. It consists of two parts, one covering theory and the other practice. The student must pass both parts before progression to the next milestone (the Qualifying Exam). Students have two attempts to pass the exam.

Candidacy exam. Upon completion of this exam, taken by the end of the 5th semester, the student is admitted to Ph.D. candidacy. The exam will assess the student’s readiness to conduct research and to communicate results competently in writing. It can take either of two forms: (1) the student is assigned a set of research papers and produces a report that synthesizes and extends the methods of the papers, or (2) the student receives an acceptance of a research paper to a peer-reviewed journal or conference. The student’s Curriculum Advisory Committee will evaluate the work produced by either method (1) or (2) and assign a passing score if justified.

Dissertation Prospectus: Students present their research findings, along with a literature review relevant to the problem addressed, along with a proposal for the remainder of the dissertation in a written prospectus. Students present the prospectus orally to their dissertation committees.

Dissertation. Students complete a significant body of research and write a dissertation summarizing the nature and significance of the work. They present the work orally before the department and defend the work to their committee.


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