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Optional MS-GL track
Take a deep dive into big data with the optional third-semester business analytics track. In addition to personalizing your MS-GL program and building new strengths, the business analytics track is uniquely positioned to benefit your career long after graduation.
The optional track requires two additional core courses and two electives, as well as a summer internship opportunity. Focused career preparation enhances your experience, providing crucial support for:
- Goal setting and action planning
- Your personal narrative and interviewing preparation
- Managing executive presence
- Navigating decision-making for a full-time offer
Business analytics courses
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.
Operations Planning and Execution
Studies management of the conversion of raw materials to finished goods including scheduling, work-in-process inventory management, and postponement/customization. Students gain a deeper understanding of the integrated supply chain of plan, source, make, deliver and return.
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 primarily numeric data.
Analytical Decision Modeling I (elective)
Explains the skills and knowledge necessary for mastery of the use of quantitative modeling tools and techniques to support a variety of business decisions. Also explores deterministic optimization techniques, including linear programming, nonlinear programming, integer programming; network models and a brief introduction to metaheuristics. Covers the use of these models for a variety of common business problems. Practical application of these models uses Excel and standalone software. Also studies how to ensure that these solutions work in a wide variety of situations (what-if analysis).
Analytical Decision Modeling II (elective)
Addresses the skills and knowledge necessary to model situations where uncertainty is an important factor. Covers models including decision trees, queuing theory, Monte Carlo simulation, discrete event simulation and stochastic optimization. Uses these models for a variety of common business problems and requires implementation of these models using Excel and standalone software. Studies how to ensure that these solutions work in a wide variety of situations (what-if analysis). Describes each of these methods in detail.
Data-Driven Quality Management (elective)
Addresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. Introduces modern quality management approaches including six sigma and design for six sigma. Covers the define, measure, analyze, improve and control (DMAIC) improvement cycle: the core process used to drive six sigma projects. DMAIC refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. Provides an analytics roadmap to help users work through the DMAIC problem-solving process.