Mathematical Sciences, MS

Program Description

Entering students are expected to have courses in linear algebra, differential equations, a computer language and statistics.

For the master’s program, both thesis and non-thesis options are available. The curriculum for both options includes foundation courses (often taken prior to entering the master’s program), a breadth requirement, and a concentration area. Additional information is available at https://www.clemson.edu/science/departments/math-stat/academics/graduate/index.html.

Graduate students in the School of Mathematical and Statistical Sciences have at least three opportunities to participate in international cooperative programs. The first is an exchange program with the Department of Mathematics at Kaiserslautern University in Germany. The second program is a two and one half month summer program in the Center for Industrial Mathematics at the University of Bremen in Germany. The third exchange program is with the Institute of Machine Sciences at the Russian Academy of Sciences (IMASH) in Moscow, Russia. More information about each of these programs is available at https://www.clemson.edu/science/departments/math-stat/academics/graduate/international-exchange-programs/index.html.

Summary of Degree Requirements

Thesis Option

The Mathematical Sciences MS thesis option degree program requires a minimum of 24 hours of graduate credit and a minimum of six hours of master’s thesis research ( MATH 8910 - Master’s Thesis Research ).

Non-Thesis Option

The Mathematical Sciences MS non-thesis option degree program requires a minimum of 30 hours of graduate credit, none of which may be master’s thesis research.

Coursework

The MS in Mathematical Sciences requires 36-37 credits in courses numbered 6000 or above (including research credits) that must be divided as follows:

Breadth requirement

The MS breadth requirement consists of six graduate courses (18 credits). Any deviation from the breadth courses listed here should be pre-approved by the Associate Director for Graduate Studies and the student’s advisor.

• Algebra: MATH 8510 - Abstract Algebra I 3 Credits  or MATH 8530 - Matrix Analysis 3 Credits  
• Analysis: MATH 8210 - Linear Analysis 3 Credits  
• Computational Mathematics: MATH 8600 - Introduction to Scientific Computing 3 Credits  
• Operations Research: MATH 8100 - Mathematical Programming 3 Credits  
• Statistics: MATH 8000 - Probability 3 Credits  or MATH 8050 - Data Analysis 3 Credits  
• Operations Research or Statistics: MATH 8000 - Probability 3 Credits , MATH 8030 - Stochastic Processes 3 Credits , MATH 8040 - Statistical Inference 3 Credits , or MATH 8140 - Network Flow Programming 3 Credits      

Concentration requirement

In addition to the satisfying the breadth requirements, students must select an identifiable concentration area and take additional courses in that area. These include algebra, analysis, computational mathematics, operations research, statistics and probability, and applied statistics and data science. For the thesis option students select four courses (12 credits) from the courses listed below, and for the project option, students select six (6) courses (18 credits) from the courses listed below.

• Algebra: MATH 8500 - Computational Algebraic Geometry , MATH 8510 - Abstract Algebra I , MATH 8520 - Abstract Algebra II , MATH 8530 - Matrix Analysis , MATH 8540 - Theory of Graphs , MATH 8550 - Combinatorial Analysis , MATH 8560 - Theory of Error-Correcting Codes , MATH 8570 - Cryptography , MATH 8580 - Number Theory , MATH 9500 - Commutative Algebra , MATH 9510 - Algebraic Number Theory , MATH 9520 - Analytic Number Theory , MATH 9540 - Advanced Combinatorics , MATH 9850 - Selected Topics in Algebra and Combinatorics , MATH 9860 - Selected Topics in Geometry  
• Analysis: MATH 8210 - Linear Analysis , MATH 8220 - Measure and Integration , MATH 8230 - Complex Analysis , MATH 8250 - Introduction to Dynamical Systems Theory , MATH 8260 - Partial Differential Equations , MATH 8270 - Dynamical System Neural Networks , MATH 8310 - Fourier Series , MATH 8370 - Calculus of Variations and Optimal Control , MATH 8410 - Applied Mathematics I , MATH 9270 - Functional Analysis , MATH 9740 - Selected Topics in Mathematical Sciences , MATH 9820 - Selected Topics in Analysis  
• Computational Mathematics: MATH 8600 - Introduction to Scientific Computing , MATH 8610 - Advanced Numerical Analysis I , MATH 8630 - Digital Models I , MATH 8650 - Data Structures , MATH 8660 - Finite Element Method , MATH 9830 - Selected Topics in Computational Mathematics  
• Operations Research: MATH 8000 - Probability , MATH 8030 - Stochastic Processes , MATH 8100 - Mathematical Programming , MATH 8110 - Nonlinear Programming , MATH 8120 - Discrete Optimization , MATH 8130 - Advanced Linear Programming , MATH 8140 - Network Flow Programming , MATH 8160 - Network Algorithms and Data Structures , MATH 8170 - Stochastic Models in Operations Research I , MATH 8180 - Stochastic Models in Operations Research II , MATH 8190 - Multicriteria Optimization , MATH 9880 - Selected Topics in Operations Research , MATH (ME) 8740 - Integration Through Optimization  
• Statistics and Probability: MATH 8000 - Probability , MATH 8010 - General Linear Hypothesis I , MATH 8020 - General Linear Hypothesis II , MATH 8040 - Statistical Inference , MATH 8050 - Data Analysis , MATH 8060 - Nonparametric Statistics , MATH 8070 - Applied Multivariate Analysis , MATH 8080 - Reliability and Life Testing , MATH 8090 - Time Series Analysis, Forecasting and Control , MATH 8810 - Mathematical Statistics , MATH 8820 - Introduction to Bayesian Statistics , MATH 8840 - Statistics for Experimenters , MATH 8850 - Advanced Data Analysis , MATH 9010 - Probability Theory I , MATH 9020 - Probability Theory II , MATH 9810 - Selected Topics in Mathematical Statistics and Probability  
• Applied Statistics and Data Science: MATH 8000 - Probability , MATH 8010 - General Linear Hypothesis I , MATH 8020 - General Linear Hypothesis II , MATH 8040 - Statistical Inference , MATH 8050 - Data Analysis , MATH 8060 - Nonparametric Statistics , MATH 8070 - Applied Multivariate Analysis , MATH 8080 - Reliability and Life Testing , MATH 8090 - Time Series Analysis, Forecasting and Control , MATH 8810 - Mathematical Statistics , MATH 8820 - Introduction to Bayesian Statistics , MATH 8840 - Statistics for Experimenters , MATH 8850 - Advanced Data Analysis , MATH 9010 - Probability Theory I , MATH 9020 - Probability Theory II , MATH 9810 - Selected Topics in Mathematical Statistics and Probability  

Research requirement

• Thesis option: Six credits of  MATH 8910 - Master’s Thesis Research  
• Project option: One credit of MATH 8920 - Master’s Project Course  

Mathematical sciences courses at the 7000-level are applicable to master’s degree programs in the School of Education only.

Outcomes, Learning Objectives, and Graduation Requirements

The mission of the MS degree program in mathematical sciences is to prepare and train the next generation of mathematical scientists. MS students familiarize themselves with a broad base of mathematical techniques from many areas of the mathematical sciences. They are involved in a significant research project and are taught to effectively disseminate their findings through written publication and oral presentation. Upon receiving the MS degree, students are competent in a broad array of mathematical science techniques and are effective communicators and teachers of basic mathematical sciences.

The MS program is structured to introduce the student to many areas of the mathematical sciences and develop deep knowledge in the student’s chosen area of specialization. Students are required to take courses satisfying a breadth requirement and a concentration component. Students must also complete and present a project or thesis.