Master in Management Science - Curriculum

Degree Requirements:

1. Technical Ability

• MS 531 - Mathematical Programming (3) Linear programming solution procedures, duality, sensitivity, and parametric analysis, linear-fractional, piecewise-linear, separable and integer programming, transportation linear programs
• MS 532 - Stochastic Models in Management Science (3) Discrete-time Markov chains, Poisson processes, continuous-time Markov chains, renewal theory, and queueing theory
• MS 533 - Computational Mathematical Programming (3) Computational aspects of mathematical programming models, in particular for large systems
• ST 563 - Statistical Inference I (3) Basic probability and probability models; random variables and distributional models; kernel density estimation; cubic splines; likelihood inference and maximum likelihood estimation and model fitting with information criteria; moment and moment generating functions; functions of random variables; goodness of fit tests and quantile modeling of distributions

Students must pass a written comprehensive examination for Management Science 531-532. A thesis option is available to qualified students.

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2. "Understanding the Business"

• MS 534  - Management Science Methods in Business (3) Application of methods from 531, 532 and 533 to real world problems in business/industry
• MS 551 - Leveraging Information Through Descriptive and Prescriptive Modeling (3) Concepts and tools for emulating business operations (descriptive modeling) and for determining optimal operational or tactical strategies (prescriptive modeling). Visualization, optimization, and simulation concepts reinforced through hands-on experience with technologies: geographic information system (GIS), spreadsheet-based models, simulation packages, and supply chain optimization software.
• OMS 541 - Operations Management (3) Techniques applicable to design of systems in operations planning and control in manufacturing and service industries.  Modeling real-world systems through problem definition, supporting data structure design, model design, solution, implementation, and maintenance.

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3. Applied Statistics Course:

One 3 hour course chosen from:

• ST 566 - Statistical Techniques in Industrial Processes (3) Applications of control charts and other statistical techniques in industrial setting.  Attributes and variables control charts, process capability analysis, aspects of sampling, statistical  tolerancing, estimation of variance components, problems of measurement, special industrial applications.
• ST 571 - Statistical Methods (3) Data collection strategies.  Descriptive statistics.  Probability distributions, simulation of random variables, sampling distributions.  Estimation and hypothesis testing, regression, Chi-Square test for categorical data, simple design of experiments, nonparametric methods.  Use of statistical software.
• ST 572 - Applied Regression Analysis (3) Simple linear regression.  Matrix approach to multiple linear regression.  Partial and sequential sums of squares, interaction and confounding, use of dummy variables, model selection.  Leverage, influence and collinearity.  Auto-correlated errors.  Generalized linear models, maximum likelihood estimation, logistic regression, analysis of deviance.  Nonlinear models, inference, ill-conditioning.  Robust regression.  M-estimators, iteratively, reweighted least squares.  Nonparametric regression, kernel, splines, testing lack of fit.
• ST 573 - Design of Experiments (3) One-factor and factorial experiments with quantitative and qualitative factors.  Checking assumptions.  Emphasis on design principles of randomization, replication, and blocking.  Analysis of covariance.  Fractional factorials and response surface designs.  Nested and split pilot designs.  Optimal design.  Industrial applications emphasized.
• ST 574 - Data Mining Methods and Applications (3) Understanding and application of data mining methods.  Data preparation; exploratory data analysis and visualization; cluster analysis; logistic regression; decision trees; neural networks; association rules; model assessment; and other topics.  Applications to real world data.  Use of standard computer packages.
• ST 575 - Applied Time Series  (3) Fundamental concepts of time series analysis:  Box-Jenkins approach, stationary and non-stationary models, forecasting model identification, seasonal models, transfer function models, and spectral theory.

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4. Information Systems Elective:

One 3 hour course chosen from

• IE 421 - Information Systems Analysis and Design (3) Systems engineering approach to analysis and design of systems of information.  Topics- system development life cycle, system analysis methodologies, data analysis techniques, system design, joint application design, and rapid application design.  Lab introduces analysis and design software tools.
• IE 514 - Advanced Information Systems Analysis and Design (3) Systems analysis and systems control concepts applied to systems of information.  Role of Industrial Engineering in office and factory of future.  Management support systems, decision support systems, and integrated support systems.
• IE 554 - Advanced Development of Information Systems (3) Presents algorithms commonly needed to implement advance information systems.  Different types of data structures and presented in an attempt to find the model that best suits a given problem,.  Includes in-depth discussion of Visual Basic modules.  Involves the transformation of problems into programming paradigms, and encodes solutions using the Microsoft Visual Basis 6 rapid application development tool.  Activities will include case studies and demonstrations to supplement lectures.  Practical problems and projects will be assigned. 

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5. Free Elective as approved by advisor

Students must choose 3 hours

6. Concentration Electives

Students must choose at least 6 hours from the following list of concentration electives

Geographic Information Systems (GIS)

• Geo 411 - Introduction of Geographic Information Science (3) Concepts and methods of spatial analysis and their application using geographic information systems software and techniques.  Emphasizes both theoretical and applied aspects of GIS.
• Geo 515 - Topics in Quantitative Geography (3) Multivariate analysis applied to problems in geography; research problems utilizing appropriate computer programs; usefulness to geographic research of techniques developed by other disciplines.
• Geo 517 - Geographic Information Management and Processing (3) Concepts and methods in management of geographic information.  Database design, manipulation, sampling and analysis.
• Geo 549 - Topics in the Geography of Transportation (3) Examination of trends, problems, and methods in transportation geography and transportation networks.
• or approved course

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Statistics

• ST 564 - Statistical Inference II (3)  Sampling distributions; point and interval estimation; fixed width entropy confidence intervals; likelihood theory; Fisher information and its inverse; large sample, deviance, and bootstrap confidence intervals; Bayesian estimation and hypothesis testing; information approach to hypothesis testing; uniformly most powerful and likelihood ration tests, theory of linear models, estimation, model building and inference.
• ST 566 - Statistical Techniques in Industrial Processes (3) Applications of control charts and other statistical techniques in industrial setting.  Attributes and variables control charts, process capability analysis, aspects of sampling, statistical tolerancing, estimation of variance components, problems of measurement, special industrial application.
• ST 571 - Statistical Methods (3) Data collection strategies.  Descriptive statistics.  Probability distributions, simulation of random variables, sampling distributions.  Estimation and hypothesis testing, regression, Chi-Square test for categorical data, simple design of experiments, nonparametric methods.  Use of statistical software.
• ST 572 - Applied Regression Analysis (3) Simple linear regression.  Matrix approach to multiple linear regression.  Partial and sequential sums of squares, interaction and confounding, use of dummy variables, model selection.  Leverage, influence and collinearity.  Auto-correlated errors.  Generalized linear models, maximum likelihood estimation, logistic regression, analysis of deviance.  Nonlinear models, inference, ill-conditioning.  Robust regression.  M-estimators, iteratively, reweighted least squares.  Nonparametric regression, kernel, splines, testing lack of fit.
• ST 573 - Design of Experiments (3) One-factor and factorial experiments with quantitative and qualitative factors.  Checking assumptions.  Emphasis on design principles of randomization, replication, and blocking.  Analysis of covariance.  Fractional factorials and response surface designs.  Nested and split pilot designs.  Optimal design.  Industrial applications emphasized.
• Or approved course

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Production and Operations/Manufacturing Management

• Eng Mgt 541 - Managing Change & Improvement in Technical Organizations (3) Current topics, theories, and applications for managing change and innovation for performance improvement in organizations.  Multi-initiative approaches:  quality management, organizational effectiveness, employee empowerment, performance measurement, and application of statistical tools and techniques.  Self-assessment and Baldrige criteria for performance excellence.  Change agent, team building, and leadership issues.  Case studies.
• IE 518 - Advanced Engineering Economic Analysis (3) Application of engineering economic analysis in complex decision situations.  Inflation and price changes; uncertainty evaluation using non-probabilistic techniques; capital financing and project allocation; evaluations involving equipment replacement, investor-owned utilities, and public works projects; probabilistic risk analysis including computer simulation and decision trees; multi-attribute decision analysis; and other advanced topics.
• MS 526 - Advanced Application of Systems Modeling and Simulation (3) Modeling of discrete, continuous, and combined systems using current simulation software.  Development of flexible simulation models to enhance accessibility of simulation models for experimentation.  Development of distributed simulation models to represent and test production and supply chain systems.
• OMS 540 Statistics and Operations Management (3) Analysis of methods and models for understanding supply chain flows processes.  Introduction to management strategies and techniques applicable to design of systems in logistics and operations processes.  Note:  this class is part of the full-time MBA core classes and meets five days a week from March until May.  The rest of your schedule would have to work around this class.
• OMS 541 Operations Management (3) Techniques applicable to design of systems in operations planning and control in manufacturing and service industries.  Modeling real-world systems through problem definition, supporting data structure design, model design, solution, implementation, and maintenance.
• Or approved course

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Health Care

• PH 520 - Public Health Policy and Administration (3) Administrative considerations of community-based health care programs and public health practice.  Health policy formulation, political environment and governmental involvement in health, legal responsibilities, and managerial concepts/techniques/process.
• PH 521 - Organization Theory and Health Care Delivery (3) Administrative and Organization theory related to health facilities; operation and management of community hospital.  Case discussions and problem-solving exercises; managerial functions and skills.
• PH 525 - Financial Management of Health Programs (3) Financial management concepts and practices applied to health services programs.  Fundamental of budgeting, costing, financing, rate setting, financial reporting and control.  Opportunities to apply techniques..
• PH 560 - Theories and Techniques in Health Planning (4) Overview of health planning concepts and methodologies; systems-oriented planning process.  Major elements of planning; formulation and conceptualization of problem, plan design, evaluation and implementation.  health problems of institutions, communities and selected population groups, appropriate diagnoses, and programs for addressing needs.
• Or approved course

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A Suggested Course of Study

Fall - Year 1		Spring - Year 1
Course	Hr	Course	Hr
ST 563	3	MS 532	3
MS 531	3	MS 533	3
Info Systems Elective	3	Applied Statistics	3
Summer - Year 1
Course	Hr
MS 534	3
Fall - Year 2		Spring - Year 2
Course	Hr	Course	Hr
OMS 541	3	Concentration. Elective	3
Concentration. Elective	3	Free Elective	3
MS 551	3		3

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