Ph.D. in Data Science and Statistics
Advancing the theory, methods and practice of data-driven research
The Ph.D. in Data Science and Statistics at SMU prepares students to develop new statistical and data science methods, address complex data-driven problems, and conduct independent research at the forefront of the field.
The program combines rigorous training in statistical theory and methodology with modern computational approaches, machine learning and artificial intelligence. Students develop a strong foundation in mathematical statistics, statistical modeling and computational methods while gaining the flexibility to pursue advanced study aligned with their research interests.
The curriculum includes foundational coursework in computational statistics, mathematical statistics, statistical analysis, statistical machine learning, artificial intelligence and machine learning, together with advanced electives in areas such as probability, stochastic processes, Bayesian statistics, time series, survival analysis, longitudinal data analysis, high-dimensional biological data, natural language processing and advanced machine learning.
Beyond coursework, students develop the skills needed to become independent researchers through doctoral research seminars, qualifying and candidacy examinations, faculty-guided research, and the completion of an original doctoral dissertation.
A Broad Foundation for Modern Research
Modern data-driven research increasingly requires both deep statistical understanding and sophisticated computational tools. The Data Science and Statistics Ph.D. is designed to provide students with both.
Students receive training in:
- Statistical theory and inference
- Statistical modeling and data analysis
- Computational statistics
- Statistical machine learning
- Artificial intelligence and machine learning
- Probability and stochastic processes
- Statistical consulting and scientific communication
- Advanced research methods tailored to individual interests
The program also provides substantial flexibility through elective coursework in Statistics and Data Science, Computer Science, Mathematics and other approved disciplines.
Preparing Independent Researchers
The program is designed for students interested in research careers in academia, industry, or government. Throughout the program, students progress from foundational coursework to advanced study and independent research.
Students must successfully complete the Ph.D. Qualifying Exams and Ph.D. Candidacy Exam before completing and defending an original doctoral dissertation.
Faculty and Research
Students work closely with faculty in the Department of Statistics and Data Science and pursue research across a broad range of areas in statistics, data science, machine learning, biomedical applications, and related fields.
Explore Faculty & Research: https://www.smu.edu/dedman/academics/departments/statistics/research
Admission Requirements
Applicants to the Ph.D. in Data Science and Statistics must hold a bachelor's degree and should have a strong mathematical foundation.
Applicants must have completed mathematics coursework through:
- Advanced calculus
- Linear algebra
The GRE General Test is optional.
Applicants must also demonstrate excellent English communication skills. Applicants who are required to submit an English language proficiency test should meet the requirements established by SMU's Moody School of Graduate and Advanced Studies.
Preparation for Doctoral Study
The program combines advanced statistical theory, methodology, and computational data science. Applicants with backgrounds in statistics, mathematics, data science, computer science, engineering, or other quantitatively rigorous disciplines may be particularly well prepared for the program.
Because preparation can vary across disciplines, applicants are encouraged to describe in their application their mathematical, statistical and computational background and how it has prepared them for doctoral study in Data Science and Statistics.
Application Information
Prospective students should submit their application through the Moody School of Graduate and Advanced Studies.
Curriculum
The Ph.D. in Data Science and Statistics requires a minimum of 64 credit hours and combines core training in statistics and data science with advanced electives and independent research. No more than 12 dissertation research credit hours may count toward the degree.
Core Curriculum
Required coursework includes:
- Computational Statistics
- Mathematical Statistics I and II
- Statistical Analysis I and II
- Statistical Machine Learning
- Statistical Consulting
- Artificial Intelligence
- Machine Learning in Python
- Doctoral Research Seminar
Students must also complete at least one advanced theory course in Probability Theory, Stochastic Processes, or Advanced Statistical Inference.
Electives
Students complete advanced electives based on their research interests. Areas of study may include:
- Bayesian statistics
- Survival and longitudinal data analysis
- Time series analysis
- Statistical methods in biomedical research
- Probability and stochastic processes
- Natural language processing
- Machine learning and neural networks
- Data mining
- Mathematical foundations of machine learning
With approval, students may also take a limited number of courses outside the Department of Statistics and Data Science when those courses have a strong statistics and data science component.
Doctoral Milestones
Students must:
- Pass the Ph.D. Qualifying Exams
- Pass the Ph.D. Candidacy Exam
- Complete and successfully defend an original doctoral dissertation
Total degree requirement: 64 credit hours
Financial Support
Students admitted to the Ph.D. in Data Science and Statistics will receive financial support through teaching assistantships, research assistantships, fellowships, or other departmental and university funding. Financial support includes a stipend, tuition support, and other benefits.