Curriculum

Last Updated:

09/02/2024 - 15:19

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The curriculum for graduate programs which is effective from Fall 2023.

MASTER OF SCIENCE CURRICULUM IN STATISTICS	DOCTOR OF PHILOSOPHY CURRICULUM IN STATISTICS
SCIENTIFIC PREPARATION	SCIENTIFIC PREPARATION
STAT 291 Statistical Programming	STAT 570 Data Handling and Visualization
STAT 363 Linear Models I	STAT 571 Data Mining and Machine Learning
STAT 295 Object Oriented Programming	STAT 572 Probability and Statistics for Data Science I
STAT 433 Statistical Machine Learning or STAT412 Statistical Data Analysis	STAT 573 Probability and Statistics for Data Science II

M.S. PROGRAM WITH THESIS	Total: 16 credits.
STAT 500 M.S. Thesis
STAT 510 Research Methods and Ethics in Statistics and Data Science	Ph.D. PROGRAM
STAT 542 Seminar I	STAT 510 Research Methods and Ethics in Statistics and Data Science*
STAT 543 Seminar II	STAT 600 PhD Thesis
	STAT 635 Advanced Computational Statistics
STAT 571 Data Mining and Machine Learning	STAT 636 Advanced Generalized Linear Models
STAT 572 Probability and Statistics for Data Science I	STAT 642 Seminar in Statistics and Data Science I
STAT 573 Probability and Statistics for Data Science II	STAT 643 Seminar in Statistics and Data Science II
STAT 8XX Special Studies	STAT 647 Probability Theory
	STAT 648 Advanced Statistical Inference
Four elective courses. Two of them could be from out of department.	STAT 8XX Special Studies (4-2) NC
Totally seven courses with at least 21 credit hours
	Five elective course(s) approved by the Department of Statistics.
M.S. PROGRAM WITHOUT THESIS	Totally nine courses with at least 29 credit hours
STAT 500 M.S. Thesis
STAT 510 Research Methods and Ethics in Statistics and Data Science	* If not taken during M.S.
STAT 542 Seminar I
STAT 543 Seminar II	INTEGRATED Ph.D. PROGRAM
	STAT 510 Research Methods and Ethics in Statistics and Data Science*
STAT 571 Data Mining and Machine Learning	STAT 542 Seminar I
STAT 572 Probability and Statistics for Data Science I	STAT 543 Seminar II
STAT 573 Probability and Statistics for Data Science II
STAT598 Term Projects in Statistics	STAT 571 Data Mining and Machine Learning
STAT 8XX Special Studies	STAT572 Probability and Statistics For Data Science I
	STAT573 Probability and Statistics For Data Science II
Seven elective courses. Two of them could be from out of department.	STAT 600 PhD Thesis
Totally ten courses with at least 30 credit hours	STAT635 Advanced Computational Statistics
	STAT636 Advanced Generalized Linear Models
Elective Courses in Statistics Department:	STAT 642 Seminar in Statistics and Data Science I
	STAT 643 Seminar in Statistics and Data Science II
STAT 504 Non-Parametric Statistical Inference and Methods	STAT647 Probability Theory
STAT 505 Sampling Theory and Methods	STAT648 Advanced Statistical Inference
STAT 509 Applied Stochastic Processes	STAT 8XX Special Studies (4-2) NC
STAT 518 Statistical Analysis of Designed Experiments
STAT 525 Regression Theory and Methods	8 elective course(s) approved by the Department of Statistics.
STAT 529 Statistical Bioinformatics	Totally fifteen courses with at least 47 credit hours
STAT 545 Longitudinal Data Analysis
STAT 553 Actuarial Analysis and Risk Theory	* If not taken during M.S.
STAT 554 Computational Statistics
STAT 557 Statistical Modeling I
STAT 558 Statistical Modeling II
STAT 559 Applied Multivariate Analysis
STAT 560 Logistic Regression Analysis
STAT 562 Univariate Time Series Analysis
STAT 563 Multivariate Time Series Analysis
STAT 564 Advanced Statistical Data Analysis
STAT 565 Decision Theory and Bayesian Analysis
STAT 566 Reliability Theory and Methods
STAT 567 Biostatistics and Statistical Genetics
STAT 568 Statistical Consulting
STAT 570 Data Handling and Visualization
STAT 574 Statistics and Data Science Computing
STAT 575 Computational Tools for Data Science
STAT 576 Neural Networks for Data Science
STAT 577 Big Data Analytics
STAT 578 Artificial Intelligence and Data Science
STAT 579 Statistical Pattern Recognition
STAT 580 Stochastic Processes in Machine Learning

To review the list of equivalent courses between the old curriculum and the new curriculum, click here.

The old curriculums for graduate programs. This curriculum is not valid after the Fall 2023 semester.

M.Sc. IN STATISTICS	Ph. D. IN STATISTICS
Curriculum for MS	Required Courses for Ph.D.
Statistics Option	STAT 601 Advanced Probability Theory I
STAT 500 MS Thesis	STAT 602 Advanced Probability Theory II
STAT 501 Statistical Theory I	STAT 603 Advanced Theory of Statistics I
STAT 502 Statistical Theory II	STAT 604 Advanced Theory of Statistics II
STAT 542 Seminar I	STAT 642 Seminar in Statistics I
STAT 543 Seminar II	STAT 643 Seminar in Statistics II
STAT 510 Research Methods and Ethics in Statistics**	STAT 510 Research Methods and Ethics in Statistics**
Three electives from Modeling, Computing or Elective Course list	Other Ph.D. Courses
Two electives from any of the following course groups or out of department	STAT 605 Theory of Linear and Nonlinear Statistical Models
Totally seven courses with at least 21 credit hours	STAT 606 Theory of Experimental Designs
	STAT 607 Nonparametric Theory of Statistics
Interdisciplinary Statistics Option for non-majors	STAT 608 Probability Models and Stochastic Processes
STAT 500 M.S. Thesis	STAT 609 Statistical Decision Theory
STAT 551 Probability and Statistics I	STAT 610 Sequential Analysis
STAT 552 Probability and Statistics II	STAT 611 Multivariate Analysis
STAT 542 Seminar I	STAT 612 Advanced Topics in Time Series Analysis
STAT 543 Seminar II	STAT 613 Advanced Topics in Life Testing and Reliability
STAT 510 Research Methods and Ethics in Statistics**	STAT 614 Interpretation of Data I
One Computing course & One Modeling course & One Elective course	STAT 615 Interpretation of Data II
Two electives from any of the following course groups or out of the department	STAT 616 Applications of Statistics in Industry
Totally seven courses with at least 21 credit hours	STAT 617 Large Sample Theory of Statistics
*Scientific Preparation Courses are at the end of the table	STAT 618 Mathematical Models and Response Surface Methodology
Computing Courses	STAT 619 Advanced Topics in Regression and Analysis of Variance
STAT 554 Computational Statistics (*)	STAT 620 Bayesian Inference
STAT 555 Advanced Computational Statistics	STAT 621 Robust Statistics
STAT 556 Advanced Computing Methods in Statistics	STAT 622 Discrete Multivariate Analysis
STAT 729 Modern Data Analysis: From Hidden Markov Models to Statistical Learning
Modeling Courses	STAT 623 Spatial Statistics
	STAT 630 Advanced Topics in Statistical Inference
STAT 503 Linear Statistical Models	STAT 632 Inference for Stochastic Processes
STAT 525 Regression Theory and Methods	STAT 634 Theory of Stationary Random Functions
STAT 557 Statistical Modeling I (*)	STAT 699 Ph.D. Thesis in Statistics
STAT 558 Statistical Modeling II (*)	STAT 730 Statistics for Bioinformatics
STAT 559 Applied Multivariate Analysis	STAT 800-899 Special Studies
STAT 560 Logistic Regression Analysis
STAT 561 Panel Data Analysis
STAT 562 Univariate Time Series Analysis
STAT 563 Multivariate Time Series Analysis
STAT 729 Modern Data Analysis: From Hidden Markov Models to Statistical Learning
Elective Courses
STAT 504 Non-Parametric Statistical Inference and Methods
STAT 505 Sampling Theory and Methods
STAT 509 Applied Stochastic Processes
STAT 518 Statistical Analysis of Designed Experiments
STAT 553 Actuarial Analysis and Risk Theory
STAT 564 Advanced Statistical Data Analysis
STAT 565 Decision Theory and Bayesian Analysis
STAT 566 Reliability Theory and Methods
STAT 567 Biostatistics and Statistical Genetics
STAT 568 Statistical Consulting
*() Starred courses can only be taken by**
the students of Interdisciplinary Statistics Option
() You don't have to take STAT 510 course again if you took the same or similar course before.**

Pool courses have been removed for interdisciplinary students who are admitted to the department in or after 2021-2022 Spring Semester.

Graduate

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