Computational Science and Statistics (Ph.D.) - Statistics Specialization

Location(s): Brookings Main Campus

Program Coordinator/Contact

Kurt D. Cogswell, Department Head
Donald Vestal, Associate Professor/Graduate Coordinator
Department of Mathematics and Statistics
Chicoine Architecture, Mathematics and Engineering Hall 209, Box 2225
605-688-6196

Program Information

The Computational Science and Statistics Ph.D. program is designed to train students to integrate computational and statistical methodologies to formulate, model, analyze, and solve research problems of interest in a wide variety of applied and theoretical contexts. The Statistics Specialization will emphasize curriculum and research programs focusing on problems at the boundaries of effectiveness of current modern statistical methods. Students in this specialization will received a multidisciplinary education combining the curriculums of the Mathematics and Data Science specializations.

Student Learning Outcomes

Students will understand mathematical and statistical models.
Students will be able to construct and apply standard mathematical and statistical models.
Students will be able to conduct research using appropriate software to implement these models.
Students will be able to disseminate the results of their research to others.
Students will be able to apply Argument Deconstruction in mathematics and statistics.

Course Delivery Format

Courses will typically be delivered in on-campus classrooms, with occasional courses offered online.

Available Options for Graduate Degrees

Doctor of Philosophy

60 Credit Plan

Core Requirements

CSS 898D - Dissertation PhD (COM) Credits: 1-36 (21 credits required)
MATH 515 - Advanced Linear Algebra (COM) Credits: 3
or MATH 770 - Numerical Linear Algebra Credits: 3
MATH 571 - Numerical Analysis I (COM) Credits: 3
MATH 625 - Advanced Calculus Credits: 3
MATH 741 - Measure and Probability Credits: 3
MATH 773 - Numerical Optimization Credits: 3
STAT 684 - Statistical Inference I Credits: 3
STAT 685 - Statistical Inference II Credits: 3
STAT 686 - Regression Analysis I Credits: 3
STAT 687 - Regression Analysis II Credits: 3
STAT 715 - Multivariate Analysis I Credits: 3
STAT 716 - Asymptotic Statistics Credits: 3
STAT 721 - Statistical Computing and Simulation Credits: 3
STAT 752 - Advanced Data Science Credits: 3

Additional Graduation Requirements

Students must also pass qualifying exams based on the program’s core sequences, and written and oral comprehensive exams as designed by the student’s advisory committee.

Additional Admission Requirements

GRE: Not required
TOEFL: 90 Internet-based
TOEFL Essentials: 9.5
IELTS: 6.5
Duolingo: 115

General Requirements

Graduate students must consult with their advisor before registering for graduate coursework.

For additional information refer to the Doctor of Philosophy Degree Requirements .