GCOM 7140 is a research-oriented class that examines how firms can leverage customer analytics to successfully create, manage, and grow brands. The class provides marketing managers and operational business leaders with the analytical tools to develop and operationally execute brand strategies that enhance customer engagement and loyalty.

Consumer Behavior and Pricing Strategy integrates our understanding of consumers from research in marketing, psychology, and behavioral economics. We will take the perspective of a marketing manager and employ this knowledge to develop, execute, and implement effective marketing strategies. Prerequisite: M.S. in Commerce students only

This course will expose you to the knowledge and skills required of, 1) brand managers as they successfully launch and manage branded products/services over time, and 2) brand consultants who consult brand managers on the best strategies and techniques for managing brands. You will also learn the process for conducting a brand audit through a group semester project.

Examines ways to design, develop and execute effective integrated, web, and social marketing programs. The course uses a business planning model which allows students to justify, build, and execute social and digital marketing programs with bottom line ROI. Students learn to identify and measure high value market segments, use web and social research to evaluate competitors and convince management of the value of digital and social marketing.

Through labs, assignments, and a capstone project, students build, evaluate, & deploy predictive ML/AI models using low-code technologies and learn how to apply these concepts to create value & support real-world applications. Students will also learn managerial considerations employing analytics initiatives to create business & societal value. No prior experience with coding, machine learning, or AI is assumed or expected.

This course aims to provide students with a practical understanding of Artificial Intelligence technology. It covers key factors for the successful development, deployment, and management of AI, machine learning, and other algorithmic approaches to automated decision-making. Students will better understand the societal impacts of AI, ethical considerations in the use of AI, the limitations of AI, and approaches to balance AI risks and benefits.

The course provides an overview of the fundamentals necessary to conduct data analytics with Python including understanding Python objects, data types, structures, packages, and data flow statements; and, reading, writing, manipulating, and plotting data. Students will perform predictive analytics via machine learning using industry-standard packages.

Students will learn how to communicate effectively with data and data structures. This includes how to evaluate potential sources of data, aggregate data values from multiple sources, and compile creative, professional, and descriptive visualizations from that data. Students will learn the best type of chart or figure for different situations and how to format those visualizations to maximize the impact to the viewer.

Multivariate statistics training to analyze Big Data sets. The course covers discrete choice modeling (logistic and probit models), classification techniques (discriminant and cluster analyses), data reduction techniques (factor analysis), and advanced predictive techniques (regression models with interactions and curvilinear effects, structural equation modeling, and factorial ANOVA). Trains students on IBM-SPSS, SAS, and R.

This course provides students with an introduction in how to effectively fill the role of Project Manager. It covers a blend of conceptual knowledge and practical skills necessary for the effective management of complex projects.