Curriculum

The course introduces students to advanced machine learning (ML) algorithms, techniques, and the practical aspects of applying them for solving prediction problems that arise in various important business contexts. We will use the Python programming language, which supports the largest data analytics ecosystem. The course emphasizes hands-on work through class exercises, homework assignments, and a final project. It covers fundamental concepts in predictive modeling, popular software tools (e.g., scikit-learn, keras, streamlit), an intuitive and powerful computational platform for analytics (Google Cloud Platform), casestudies that illustrate how industry leaders leverage predictive modeling to improve decision-making and gain competitive edge, as well as emerging topics in ML such as interpretable ML and explainable AI. The course is self-contained but basic Python programming proficiency is expected.

This course introduces the field of AI to business students with a particular emphasis on deep learning which is driving the current AI revolution. The course consists of three modules. The first module establishes the foundation of AI and deep learning. The second module introduces major neural network architectures widely used in practice. The third module touches on generative AI which is particularly promising in recent years. The course emphasizes experiential learning and contains many hands-on projects using TensorFlow broadly and Keras in particular.

This course draws on a unique blend of social media strategies and the rapidly evolving information technologies supporting these strategies. We will discuss issues related to the monitoring and analyzing of social media for companies in different industries. The learning objectives of this course include: (1) gaining a deeper understanding of social media and its implications in the business world;(2) becoming comfortable with text data; and (3) being able to understand and apply several commonly used methods to analyze text data.

GBA478 covers the application of generative AI technologies across diverse business contexts. The course will help you understand how to integrate Generative AI into today’s business workflows, providing frameworks to decide when and how to use it effectively. You’ll gain hands-on experience designing Generative AI tools to create business value and programming basic LLM-driven applications in Python. Finally, the course will ask you to become conversant with the big questions about Generative AI, to debate the moral, philosophical, and ethical challenges inherent in these systems and technologies.

Product Management requires outstanding business skills, strong cross-functional leadership, and the ability to discover market/customer needs and to develop profitable solutions to meet those needs. It also requires a set of very specific applied skills, and this course is intended to develop those skills.

The course studies buyer behavior in consumer and industrial markets. Topics include culture, social class, consumer involvement, motivation, knowledge, attitudes, and group decision making. Besides theory, the course also covers applications to product, advertising, and pricing decisions.

This course examines the major issues involved in marketing on the Internet. Among the topics studied are: new product opportunities on the Internet; the changed role of advertising; the Internet as a two-way communication medium with consumers; targeting individual consumers; word-of-mouth among consumers on the Internet; the Internet as a distribution channel; and marketing research on the Internet.

Students will learn the major differences in pricing strategies between selling to consumers and to other firms which then deal with consumers. The course starts by analyzing the pricing problem of a manufacturer selling to a retailer. We examine the issue of double marginalization and learn how two-part tariffs get us out of this problem. We also examine different forms of contractual relations—from vertical acquisitions to regular short-term contracts—and potential issues with every form, touching on transfer pricing and outsourcing. In the second part of the course, we analyze a crucial concept of cost pass-through (how much a retailer should decrease the retail price in response to a decrease in the wholesale price) and the effect of manufacturer’s advertising on the retailer and on the channel overall.
 

This course builds on MKT 414/STR 423 to equip students with the necessary skills to make profitable pricing decisions in complex business environments. Topics include pricing with constrained supply, pricing in the presence of uncertainty about demand, markdown management, advance selling, pricing on the internet, pricing in the presence of direct or indirect network effects, selling through auctions, and behavioral and ethical aspects of pricing. The course also includes a comprehensive pricing simulation.

The objective of this course is to prepare students with the intuition and tools to make pricing recommendations, and to meet the booming demand in pricing and consulting related careers. The course builds around applying state-of-the-art data analysis toolkit to data of historical sales to predict underlying consumer demand. We study analytical methods suitable for different types of data structure and different pricing objectives. Topics covered include pricing against competing products, product positioning and finding market segments.

This course focuses on key marketing research topics related to consumer and brand measurement and the analysis of this information to generate insights leading to improved marketing decisions. Class time will consist of lectures by the professor and guests concerning key consumer measures and related analysis tools and methodologies.