- Home
- About
- Programs & Policies
- Enrollment & Registration
- Graduation & Honors
- Student Resources
- Faculty & Staff

The degree of Master of Science in Industrial Mathematics is designed to produce generalized problem solvers of great versatility, capable of moving within an organization from task to task. The graduate will have acquired not only the standard mathematical and statistical tools and computer science principles to strengthen data analytic skills, but also the basic ideas of engineering and business, and will have received training in project development and in modes of industrial communication. The program is designed for students planning careers in business, government or industry.

**Admission**

To be admitted to the Master of Science in Industrial Mathematics program, a person should have completed (1) the mathematics or applied mathematics courses normally required for the bachelor’s degree with a major in mathematics, statistics, economics, physics or engineering, (2) courses at the senior level in mathematical analysis, linear algebra and differential equations, and (3) have some familiarity with mathematical software programs such as Mathematica, Matlab, etc.

Students entering the program are expected to have a mathematical preparation at the level of Mathematics 421, 414 and 442. Students with deficiencies may be required to take additional course work.

**Requirements for the Master of Science Degree in Industrial Mathematics**

In addition to meeting the requirements of the University and the College of Natural Science, the student must complete a total of 30 credits for the degree under Plan B (without thesis). The student’s program of study must be approved by the student’s academic advisor, including:

1. | The following requirements for the major (30 credits): | ||||||

a. | Both of the following courses: | ||||||

MTH | 843 | Survey of Industrial Mathematics | 3 | ||||

MTH | 844 | Projects in Industrial Mathematics | 3 | ||||

b. | A minimum of two of the following courses: | ||||||

MTH | 810 | Error-Correcting Codes | 3 | ||||

MTH | 841 | Boundary Value Problems I | 3 | ||||

MTH | 842 | Boundary Value Problems II | 3 | ||||

MTH | 847 | Partial Differential Equations I | 3 | ||||

MTH | 848 | Ordinary Differential Equations | 3 | ||||

MTH | 849 | Partial Differential Equations | 3 | ||||

MTH | 850 | Numerical Analysis I | 3 | ||||

MTH | 851 | Numerical Analysis II | 3 | ||||

MTH | 852 | Numerical Methods for Ordinary Differential Equations | 3 | ||||

MTH | 880 | Combinatorics I | 3 | ||||

MTH | 881 | Graph Theory | 3 | ||||

c. | A minimum of two of the following courses: | ||||||

STT | 801 | Design of Experiments | 3 | ||||

STT | 802 | Statistical Computation | 3 | ||||

STT | 843 | Multivariate Analysis | 3 | ||||

STT | 844 | Time Series Analysis | 3 | ||||

STT | 847 | Analysis of Survival Data | 3 | ||||

STT | 861 | Theory of Probability and Statistics I | 3 | ||||

STT | 862 | Theory of Probability and Statistics II | 3 | ||||

STT | 863 | Statistics Methods I | 3 | ||||

STT | 864 | Statistics Methods II | 3 | ||||

STT | 866 | Spatial Data Analysis | 3 | ||||

STT | 875 | R Programming for Data Sciences | 3 | ||||

STT | 886 | Stochastic Processes and Applications | 4 | ||||

STT | 888 | Stochastic Models in Finance | 3 | ||||

d. | At least two of the following courses: | ||||||

CMSE | 801 | Introduction to Computational Modeling | 3 | ||||

CMSE | 802 | Methods in Computational Modeling | 3 | ||||

CMSE | 820 | Mathematical Foundations of Data Science | 3 | ||||

CMSE | 821 | Numerical Methods for Differential Equations | 3 | ||||

CMSE | 822 | Parallel Computing | 3 | ||||

CMSE | 823 | Numerical Linear Algebra | 3 | ||||

CSE | 802 | Pattern Recognition and Analysis | 3 | ||||

CSE | 803 | Computer Vision | 3 | ||||

CSE | 830 | Design and Theory of Algorithms | 3 | ||||

CSE | 835 | Algorithmic Graph Theory | 3 | ||||

CSE | 836 | Probabilistic Models and Algorithms in Computational Biology | 3 | ||||

CSE | 841 | Artificial Intelligence | 3 | ||||

CSE | 847 | Machine Learning | 3 | ||||

CSE | 860 | Foundations of Computing | 3 | ||||

CSE | 872 | Advanced Computer Graphics | 3 | ||||

CSE | 880 | Advanced Database Systems | 3 | ||||

CSE | 881 | Data Mining | 3 | ||||

CSE | 885 | Artificial Neural Networks | 3 | ||||

EC | 811A | Mathematical Applications in Economics | 2 | ||||

EC | 811B | The Structure of Economic Analysis | 2 | ||||

EC | 812A | Microeconomics I | 3 | ||||

EC | 812B | Microeconomics II | 3 | ||||

EC | 813A | Macroeconomics I | 3 | ||||

EC | 813B | Macroeconomics II and its Mathematical Foundations | 4 | ||||

EC | 820A | Econometrics IA | 3 | ||||

EC | 820B | Econometrics IB | 3 | ||||

EC | 821A | Cross Section and Panel Data Econometrics I | 3 | ||||

EC | 821B | Cross Section and Panel Data Econometrics II | 3 | ||||

EC | 822A | Time Series Econometrics I | 3 | ||||

EC | 822B | Time Series Econometrics II | 3 | ||||

ECE | 848 | Evolutionary Computation | 3 | ||||

ECE | 863 | Analysis of Stochastic Systems | 3 | ||||

ME | 830 | Fluid Mechanics I | 3 | ||||

ME | 840 | Computational Fluid Dynamics and Heat Transfer | 3 | ||||

ME | 872 | Finite Element Method | 3 | ||||

MKT | 805 | Marketing Management | 2 | ||||

MKT | 806 | Marketing Research for Decision Making | 3 | ||||

MKT | 816 | Marketing Analysis | 3 | ||||

MKT | 819 | Advanced Marketing Research | 3 | ||||

MKT | 864 | Data Mining in Marketing | 3 | ||||

SCM | 800 | Supply Chain Management | 3 | ||||

SCM | 815 | Emerging Topics in Supply Management | 3 | ||||

SCM | 826 | Manufacturing Design and Analysis | 1.5 | ||||

SCM | 833 | Decision Support Models | 2 | ||||

SCM | 843 | Sustainable Supply Chain Management | 2 | ||||

SCM | 853 | Operations Strategy | 2 | ||||

SCM | 854 | Integrated Logistics Systems | 1.5 | ||||

e. | Completion of a Certificate Program in Project Management. This requires completion of PHM 857 Project Management, covering such topics as formal project management culture, principles, knowledge areas, and terminology. It will normally be undertaken during the first year of enrollment with the opportunity to use the credit-no credit grading system. Certification will also require participation in Industrial Mathematics-specific discussion sessions. After the completion of the certificate program is approved by the instructors, the Industrial Mathematics Program, and the Associate Dean of the College of Natural Science, the Office of the Registrar will enter on the student’s academic record the name of the certificate program and the date it was completed. This certification will appear on the student’s transcript upon completion of the requirements for the degree program. |