MSA 8000 Consumer and Managerial Decision Making
Credit Hours 3.0
Description

Prequisites: ECON 2105 AND ECON 2106 or MBA 7035.
This course presents a microeconomic framework of consumer and managerial decisions from which analytically informed strategies can be developed. The first part presents a model of consumer preferences and how individuals make purchasing choices for products or services. Topics include preferences and utility theory, demand analysis, and the impact uncertainty and incomplete information have on consumer decisions. The second part extends the theory of individual choice to corporate managerial decision-making. Topics covered include risk and return analysis, cost of capital, project selection, and capital budgeting techniques. Illustrative applications using large data will be included as necessary.

MSA 8050 Unstructured Data Management
Credit Hours 3.0
Prerequisites CIS 8040
Description

This course addresses the unstructured data management skills needed for modern data analysis including those salient to big data and real-time data environments. The focus is on unstructured data and its environment. Unstructured data includes web data (blogs, text), user generated content, social media, location-aware data, and digital media among others. Topics covered include extraction methods for real time audio and video data, data capture, cleaning, representation, storage, queries, manipulation, and real-time data management. Also included as they apply to unstructured data environment are data security, governance, and visualization. Students will learn natural language processing and geo-spatial analytical tools.

MSA 8100 Operations Research Models and Methods
Credit Hours 3.0
Prerequisites ECON 8710 or MRM 8000
Description

The focus of this course is operations research (OR) as a discipline of applying advanced analytical methods to help make better business decisions. It introduces formulation, solution techniques, and sensitivity analysis for optimization problems that include linear, integer, network flow, non-linear and dynamic programs such as traditional LP/ILP/MILP models, transportation and network models. Students are exposed to multidisciplinary applications from areas including but not limited to logistics, manufacturing, transportation, marketing, project management, health care, urban planning, and finance. Students use software packages to solve linear, integer, and network problems.

MSA 8200 Econometric Modeling for Analytics
Credit Hours 3.0
Prerequisites ECON 9720 or consent of the instructor
Description

This course introduces students to econometric methods used in business analytics with a focus on real-world applications and datasets. The course covers two primary topics: econometric methods for panel data including how to account for basic heterogeneity effects; the most important models used for the analysis of time series including estimation and inference methods for univariate and vector auto-regressive models. After discussing these models in the classical context, the course revisits them using Bayesian methods with a focus on issues of parameter and model uncertainty. The course closes with a discussion of state-space models and Kalman filtering.

MSA 8300 Value Through Analytics: Model Deployment and Life Cycle Mgmt
Credit Hours 3.0
Prerequisites MGS 8040
Description

This course serves as a practicum to apply aspects of the life cycle of a predictive model with real data. Students review all phases of the cycle to identify the need for models based on the business situation, define the appropriate inputs to the model, identify sources of data, and prepare data for modeling. They develop and validate the model, and discuss strategies for deployment. They develop and put in place processes for testing and monitoring the quality of the models to ensure optimal performance. Champion/challenger strategies and standardized as well as custom monitoring reports are discussed. As models degrade over time, strategies for updating and replacing models and assessing the business benefit over time will be addressed to complete the model life cycle.