Linear programming decision models, solutions, duality, sensitivity analysis, linear and integer optimization models, transportation and selected network flow models, along with application issues of these models.
Principles and techniques for managing operations in a lean supply chain. Emphasis on process improvement techniques such as lean thinking and the theory of constraints using hands-on simulations.
• Statistics 565 (5 credits) Quantitative Methods for Business Analytics
Probability and probability models, random variables (univariate and multivariate), moments and moment generating functions, likelihood inference and maximum likelihood estimation. Mathematical methods for probability and statistical inference.
• Statistics 571 (3 credits) Statistical Methods for Business
Data collection strategies including simple design of experiments. Structured querying language. Descriptive statistics. Estimation and hypothesis testing. Importance of assumptions. Quantile plots and goodness of fit. Prediction intervals. ANOVA, linear regression, chi-square tests for categorical data, logistic regression. Use of statistical and database software.
• Elective for those without business background: Managerial Accounting 1
Understand the methods and techniques used by managers to solve business problems using accounting data and to plan and assess business operations.
Software utilized in fall semester courses: Excel, JMP, MySQL, Tableau
• Management Science 532 (3 credits) Simulation and Decision Analytics
Monte Carlo and discrete-event simulation for decision-making
Visual Basic for Applications (VBA)
• Statistics 566 (3 credits) Introduction to Data Management and Directed Process Studies
Retrieving, manipulating, and merging data from a variety of database structures. Sampling and subgrouping methods for directed study of process variation. Topics include common/special cause models, components of variance, spatial variation, process mapping.
• Statistics 572 (3 credits) Applied Regression Analysis for Business
Matrix approach to multiple linear regression. Normal equations, interaction and confounding, use of dummy variables, model selection. Leverage, influence and collinearity. Autocorrelated errors. Logistic regression, maximum likelihood estimation, analysis of deviance, retrospective studies. Tree based models for discrete and continuous outcomes. Robust regression, and weighted least squares. Applications involving predictive modeling for credit risk and customer acquisition. Case studies from accounting, finance, and marketing.
• Statistics 574 (3 credits) Data Mining for Business Applications
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 business problems.
• Elective: Supply Chain Logistics 1: Strategic Issues in Supply Side Supply Chain Management
Strategic logistics-related management issues and frameworks associated with managing the supply side of contemporary supply chains. Topics such as procurement, strategic sourcing, inbound logistics, MRP and inventory management will be discussed.
New software utilized in spring semester courses: SAS Enterprise Guide, SAS Enterprise Miner, SAS macros and Proc SQL, VBA for Excel.
Fall semester, 2014 (12 credits)
• Management Science 534 (3 credits) Business Analytics Experience
This is the capstone project, where teams of 3-4 students complete a prescribed project for a business.
• 3 Electives (3 credits each) Some of the options are:
Categorical Data Analysis
Design of Experiments
Multivariate and Data Mining Techniques (includes text mining)
Supply Chain Analytics
Systems Optimization Analytics
The Masters in Business Analytics is an intensive, three semester curriculum. The foundation of the curriculum is outlined above.