Statistics and Data Science

www.smu.edu/statistics

Professor Daniel Heitjan, Department Chair

Professors: Ronald Butler, Jing Cao (Director of Graduate Studies), Daniel Heitjan (Director of the Biostatistics Ph.D. Program)
Associate Professors: Ian Harris, Monnie McGee
Assistant Professors: Sy Han (Steven) Chiou, Marcin Jurek, Chul Moon, Raanju Sundararajan
Technical Assistant Professor: Bivin Sadler
Assistant Professor of Practice: Charles South
Senior Lecturer: Stephen Robertson
Lecturers: Ashley Edison, Jessica Wickersham

General Information

The Department of Statistics and Data Science offers the following graduate degree programs: the Ph.D. in statistical science, the Ph.D. in biostatistics, the Ph.D. in data science, the M.S. in applied statistics and data analytics, and the M.S. in data science.

The courses in the biostatistics and statistics Ph.D. curricula provide students with a strong foundation in mathematical statistics, statistical computing, and probability, and statistical methods useful in statistical practice. The Ph.D. in biostatistics is conferred by the Department of Statistics and Data Science at SMU in partnership with the University of Texas Southwestern Medical Center at Dallas. Students attain a strong mathematical and statistical foundation such as that provided in the Ph.D. in statistical science curriculum, but they also take courses and engage in research projects that prepare them for a research career in biostatistics. The Ph.D. in data science draws its core material from the doctoral curricula in statistics and computer science. Students apply this knowledge in the solution of challenging applied problems from a range of disciplines.

The M.S. in applied statistics and data analytics (MASDA) degree program provides students with a theoretically-based understanding and proficiency in statistical methods, as well as training in statistical computing, database management, predictive methods, and other data science techniques.

The M.S. in data science (MSDS) is an online degree program that provides students with a theoretically-based understanding and proficiency in the management, analysis, mining, and interpretation of complex databases to support strategic decision-making.

Note: In all three doctoral programs, a student advances to candidacy after passing comprehensive and qualifying exams, preparing a written prospectus, and giving an oral presentation in a research area on which the dissertation will be based.

Programs

• Biostatistics, Ph.D.
• Data Science, Ph.D.
• Statistical Science, Ph.D.

• Applied Statistics and Data Analytics, M.S.
• Data Science, M.S.D.S.
• Statistical Science, M.S.

Courses

• DS 6110 - Immersion
• DS 6120 - Capstone A
• DS 6130 - Capstone B
• DS 6306 - Doing Data Science
• DS 6370 - Statistical Sampling
• DS 6371 - Statistical Foundations for Data Science
• DS 6372 - Applied Statistics: Inference and Modeling
• DS 6373 - Time Series
• DS 6390 - Visualization of Information
• DS 6391 - Visualization of Information and Creative Coding II
• DS 7330 - File Organization and Database Management
• DS 7331 - Machine Learning I
• DS 7333 - Quantifying the World
• DS 7335 - Machine Learning II
• DS 7337 - Natural Language Processing
• DS 7346 - Cloud Computing
• DS 7347 - High Performance Computing
• DS 7349 - Data and Network Security
• DS 7374 - Business Analytics
• DS 8310 - Independent Study

• STAT 6049 - Graduate Full-Time Status
• STAT 6110 - Independent Study in Statistical Science
• STAT 6301 - Experimental Statistics I
• STAT 6302 - Experimental Statistics II
• STAT 6306 - Introduction to Data Science
• STAT 6307 - Introduction to Statistical Computing
• STAT 6309 - Machine Learning Using Python
• STAT 6310 - Independent Study in Statistical Science
• STAT 6311 - Introduction to Mathematical Statistics I
• STAT 6312 - Introduction to Mathematical Statistics II
• STAT 6315 - Survey Sampling
• STAT 6324 - Computational Statistics
• STAT 6327 - Mathematical Statistics I
• STAT 6328 - Mathematical Statistics II
• STAT 6336 - Statistical Analysis I
• STAT 6337 - Statistical Analysis II
• STAT 6341 - Sports Analytics
• STAT 6345 - Linear Regression
• STAT 6346 - Advanced Regression Analysis
• STAT 6350 - Analysis of Lifetime Data
• STAT 6355 - Applied Multivariate Analysis
• STAT 6357 - Categorical Data Analysis
• STAT 6358 - Statistical Analysis of High Throughput Biological Data
• STAT 6360 - Statistical Methods in Epidemiology
• STAT 6363 - Time Series Analysis
• STAT 6366 - Statistical Consulting
• STAT 6367 - Statistical Practice
• STAT 6370 - Stochastic Models
• STAT 6371 - Probability Theory
• STAT 6376 - Stochastic Processes
• STAT 6377 - Multivariate Categorical Data
• STAT 6380 - Mathematical Theory of Sampling
• STAT 6385 - Survey of Nonparametric Statistics
• STAT 6390 - Bayesian Statistics
• STAT 6391 - Bayesian Hierarchical Modeling
• STAT 6395 - Selected Topics in Statistics
• STAT 6397 - Statistical Methods in Clinical Trials
• STAT 6398 - Thesis
• STAT 7011 - Supervised Internship
• STAT 7012 - Supervised Internship
• STAT 7013 - Supervised Internship
• STAT 7100 - Seminar
• STAT 7110 - Supervised Internship
• STAT 7111 - Seminar in Statistical Literature
• STAT 7112 - Seminar in Statistical Literature
• STAT 7300 - Seminar
• STAT 7327 - Advanced Statistical Inference
• STAT 7331 - Modeling Longitudinal and Incomplete Data
• STAT 7362 - Topics in Statistics
• STAT 8049 - Graduate Full-Time Status
• STAT 8105 - Research
• STAT 8198 - Dissertation
• STAT 8398 - Dissertation
• STAT 8698 - Dissertation