Course Descriptions

CIS 505: Introduction to Enterprise Analytics
Ensuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes.

SCM 516: Introduction to Applied Analytics
An introduction to the use of quantitative modeling tools and techniques to solve problems faced in modern supply chains. Students are required to implement the appropriate quantitative methods in an Excel workbook, including forecasting demand, determining the capacity of a manufacturing line and the cycle times of parts being processed on the line, and methods to manage inventory.

CIS 508: Data Mining I
Charting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from numeric data.

ECN 525: Applied Regression Models
Leverage the most recent versions of available statistical tool suites to report statistical findings to lay persons and stakeholders. Topics will include, but are not limited to: multiple linear regression, models for quantitative and qualitative predictors, building regression models, autocorrelation, non-linear regression, piecewise linear regression, inverse prediction, weighted least squares, ridge regression, robust regression and non-parametric regression.

SCM 517: Data-Driven Quality Management
Addresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. The course introduces modern quality management approaches including Six Sigma and Design for Six Sigma, and covers DMAIC, the implementation cycle used to drive Six Sigma projects. DMAIC combines the five critical process improvement steps: Define, Measure, Analyze, Improve and Control. An analytics roadmap to help users work through the DMAIC problem solving process is provided.

SCM 518: Analytical Decision Making Tools I
The first of two courses focused on the mastery of quantitative modeling tools and techniques for business decision-making, this course focuses on deterministic optimization techniques. This includes linear, nonlinear, and integer programming, network models, and an introduction to metaheuristics, with a focus on applying these tools to real business problems. Students will be required to implement these models using Excel and standalone software.

CIS 509: Data Mining II
Learn how to support informed decision making and extract predictive analytics and patterns from nonnumeric data by leveraging tools and techniques to analyze unstructured data.

SCM 519: Analytical Decision Making Tools II
This part-two course addresses the skills and knowledge necessary to model situations where uncertainty is a major factor. Models include decision trees, queuing theory, Monte Carlo simulation, discrete event simulation, and stochastic optimization, along with application for solving a wide variety of common business problems. Students will be required to implement these models using Excel and standalone software.

CIS 515: Business Analytics Strategy
Evaluate, strategically align, plan for and direct investments in, and governance of, processes for continuous renewal of analytic deployments in business.

SCM/CIS 593 (1.5 CH ea.): Applied Project
The Applied Project is the culminating experience of the program, in which you will address a problem in a domain where analytics solutions have not yet advanced to a point of wide-scale adoption. You will gain real-world experience through projects drawn from real business data that addresses an important new frontier of an organization’s analytics deployment. You will be challenged to identify relevant tool suites and analytics frameworks, and make data-driven conclusions and discoveries. In addition, your end-to end project will offer challenges that may include messy data sources and undefined business value, which will develop and advance your communication skills and leadership abilities. This team-based research project is intended to push the envelope of your skills to under-explored, applied domains.