Computational Science and Statistics

Study mode:On campus Study type:Full-time Languages: English
Local:$ 210 / Year(s) Foreign:$ 445 / Year(s)  
StudyQA ranking:966 Duration:36 months You need IELTS certificate

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The focus of Computational Science and Statistics (CSS) PhD Program is the development of sophisticated mathematical and statistical models and their implementation on high performance computing platforms. The program is research intensive. Coursework requirements are flexible, allowing a program of study to be designed to support research activity in a wide variety of areas.

Current areas of faculty and graduate student research activity include bioinformatics, biostatistics, business and financial analytics, computational biology, computational finance, molecular dynamics simulation, optimization, and statistics. The program is particularly effective at preparing graduates to work in business, industry, or government as well as the more traditional area of higher education.

Specialize in highly applicable areas with excellent employment prospects

Available specializations include predictive analytics, forensic statistics, operations research, statistics, bioinformatics, and computational modeling.


Courses include:

* MATH 571 Numerical Analysis I
* MATH 575 Operations Research I
* MATH 725 Advanced Calculus I
* MATH 751 Applied Functional Analysis (odd years)
* MATH 773 Numerical Optimization (even years)
* STAT 784 Statistical Inference I
* STAT 785 Statistical Inference II
* STAT 786 Regression Analysis I
* STAT 761 Design of Experiments (Fall '14 only)
* STAT 751 Predictive Analytics II
* STAT 736 Bioinformatics
* STAT 716 Asymptotic Statistics (starting Fall '15)
* STAT 752 Advanced Data Science (starting Fall '15)
* STAT 701 Modern Applied Statistics I
* STAT 560 Time Series Analysis
* STAT 541 Statistical Methods II
* STAT 5xx One Credit Modular Courses - Topics TBD
* MATH 771 Numerical Analysis II (odd years)
* MATH 770 Numerical Linear Algebra (even years)
* MATH 775 Operations Research II
* MATH 741 Measure & Probability
* MATH 716 Algebraic Structures I
* STAT 510 SAS Programming I
* STAT 721 Statistical Computation and Simulation
* STAT 787 Regression Analysis II
* STAT 761 Design of Experiments (starting SP '16)
* STAT 551 Predictive Analytics I
* STAT 535 Applied Bioinformatics
* STAT 715 Multivariate Statistics
* STAT 742 Spatial Statistics (odd years)
* STAT 731 Biostatistics (even years)
* STAT 702 Modern Applied Statistics II
* STAT 545 Nonparametric Statistics
* STAT 541 Statistical Methods II
* STAT 5xx One Credit Modular Courses - Topics TBD


Applicants for the Ph.D. program should have a Master's degree in mathematics, statistics, bioinformatics, computer science, or a closely related field (such as biology, chemistry, or basic biomedical sciences).
Applicants from the latter fields should have strong backgrounds in at least two out of the three disciplines of mathematics, statistics, and computer science.
Students should submit an online application to the SDSU Graduate School, and send two letters of recommendation, a CV, and a brief statement of research interests directly to the Department of Mathematics and Statistics.
An undergraduate GPA of 3.0 or better, based on a 4.0-point scale is required for full admission.

English Language Requirements

TOEFL paper-based test score : 575 TOEFL iBT® test: 90


Assistantships and fellowships are available
Many graduate research and teaching assistantships and fellowships are available, though applicants should note that admission to the CSS PhD Program does not guarantee that an assistantship or fellowship will be awarded.



South Dakota State University has been granted the full, ten-year reaccreditation by the Higher Learning Commission. An accreditation team of representatives from peer institutions commended SDSU for:

* Dedicated faculty and staff
* Quality new facilities
* Excellent student leadership
* Shared governance
* Partnerships with local community
* Robust and Integrated assessment program
* Successful transition to NCAA Division I Athletics

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