Operations Management (716)

53:716:502 Business Analytics (3) Analytic competency is becoming tremendously important in the business world and is often the factor that distinguishes leading firms in any industry. This course is intended to provide an introductory overview of how firms implement data-driven decision-making. Students will learn statistical concepts, use spreadsheet modeling, and learn through a mix of lectures, cases, and class discussions. Students are required to have a functioning computer with Microsoft Excel installed. Within Excel, you must have DATA ANALYSIS and SOLVER functionality. The course's primary goal is to coach students on fact-based decision-making and enable them to carefully plan and run business experiments to make informed managerial decisions.

53:716:513 Operations and Supply Chain Management (3) This course aims to (1) familiarize students with the major operational issues confronting managers, and (2) provide students with concepts, insights, and tools to deal with these issues. Topics include inventory management, capacity planning, forecasting, quality management, lean systems, supply chain management, and logistics.

53:716:516 Total Quality Management (3) This course provides the development, practice, and processes of quality management. It focuses on increasing productivity through continuous improvements in quality. Case studies and role-playing exercises are used in the instruction. Prerequisite: 53:716:513.

53:716:521, 522 Directed Study in Operations Management (3,3) Supervised by an individual faculty member and approved by the associate dean of graduate studies. Prerequisite: As determined by instructor.

53:716:535 Big Data Analytics (3) This course provides an in-depth introduction to analytics in the context of big data, an environment with data of size, scale and variety not handled by traditional databases. The course also provides exposure to relevant big data technologies and algorithms such as clustering, classification, and association rules. The concepts will be augmented with hands-on skills and illustrated with select business applications chosen from a range that span decision making, prediction, enhanced business intelligence, and deriving business value from big data opportunities. Prerequisite: 53:716:502

53:716:540 Social Media and Sentiment Analysis (3)

This course enables students to ingest Big Data from APIs for social media platforms such as Twitter. After assembling data from social media, students learn to analyze the data to gain business insights. Concepts for the analysis of social media, such as community detection and assignment, node centrality, information diffusion, and opinion formation will be presented. Students will also learn the process for sentiment extraction, opinion mining, and recognizing opinion spam.

Note: Students should have familiarity with the Python programming language before coming into this course.

Prerequisites: 53:716:502 and 53:623:517.

53:716:545 Machine Learning Application (3)

The focus of the course will be to introduce basic concepts in machine learning and data-analytic thinking to students, with an applied business orientation. Students will understand how to use data to competitive advantage and to build and evaluate models for decision-making. Companies today have access to vast amounts of data from their business operations. Data science is the craft of extracting patterns from this data and using available information for competitive advantage. This course represents an introduction to data science and data analytic thinking. Students will learn to leverage data to answer business questions relating to classification tasks (e.g., will this credit card prospect default or not?).

Note: Familiarity with Python programming language as well as a working knowledge of Jupyter notebooks.

Prerequisite: 53:716:502

53:716:550 Supply Chain Analytics (3) This course illustrates how the field of data analytics can be applied to optimally manage supply chains. Students learn to apply data driven decision-making methodology to the field of supply chain management. Topics will encompass all portions of a supply chain including sourcing, procuring, buying, making, moving, and selling. Topics will include designing and planning supply chains, transportation analysis, facility and warehouse location models, demand and inventory management, and supply chain risk analytics. Case studies and hands-on assignments will introduce students to current business applications and innovative use of these ideas. Prerequisite: 53:716:502.

53:716:597 Internship in Operations Management (BA) An internship provides real-world experience to those looking to explore or gain the relevant knowledge and skills within the management field. Prerequisite: Approval of instructor or associate dean for graduate programs

53:716:670 Special Topics (3) Designed to integrate course materials, introduce newer philosophies and techniques in operations management, and apply them to selected problems. Topics vary from semester to semester. Prerequisite: As determined by faculty.

53:716:671 Special Topics in Operations Management (3) Designed to integrate course materials, introduce newer philosophies and techniques in operations management, and apply them to selected problems. Topics vary from semester to semester. Prerequisite: As determined by faculty.