Curriculum

The rise of social media has empowered customers in an unprecedented way. They are well connected with each other through platforms like Facebook and Twitter, and they can easily express and distribute their comments, criticisms, or endorsements publicly to large audiences in real time. This fundamental media revolution not only forces companies to actively manage their presence and engage with customers on social media platforms but also offers them a golden opportunity to extract intelligence from the vast amount of unstructured data. Technology and strategies are increasingly intertwined in this new frontier of innovation and competition. 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.

This course provides MS and MBA students with the opportunity to use the skills they have developed through other coursework in statistics and analytics to the development and execution of a capstone project. The projects, using realworld situations and data, will serve as preparation for careers in industries such as marketing, consulting, and finance that require extensive knowledge and application of data science.

This course focuses on database design, management, and warehousing concepts to support AI and analytics efforts. The course introduces ER diagrams, SQL programming and related frameworks for data retrieval and transformation as well some advanced SQL features such as user-defined functions, triggers and procedures.

The course will introduce and apply frameworks related to answering these questions. The course will use a series of real-world cases and examples involving managerial decisions to connect analytics to real world problems. These cases will take the students through an active learning process that builds on and extends the skills and knowledge gained in previous courses. The course focuses heavily on “soft” skills related to structuring complex problems, decision-making with imperfect information, and designing analysis to address analytics research questions. The course also reinforces and expands on “hard” skills related to data wrangling, programming, descriptive, predictive, and causal analytics, as well as introducing some unsupervised learning methods. The course will leverage software tools such as sql, Python, and Tableau.

This course is fundamentally about how to learn from data. We will demonstrate the stark differences in the goals and implementations of descriptive, predictive, and causal analysis. Students will be expanding their analytic toolkit in each of these areas and learning how to select the right tool for the job. Particular attention will be paid to the usage of these analyses to drive business decisions, and how to respond to data analytics questions in job interviews.

This course equips MS students with statistical skills necessary for data-driven decision making. The course covers central tendency and variability, probability, binomial and normal distributions, standard scores, hypothesis testing, z and t tests, ANOVA, correlation and regression, and non-parametric tests.

This course introduces students to the basics of economics and marketing strategy through interactive lectures and case discussions. Consumer choice, demand curves, the impact of competition and costs form the nucleus of the economics topics. Marketing strategy builds on these consumer, competition and company considerations to understand the segmentation, targeting, positioning and promotional decisions of the firm.

This course provides a foundation in programming within the Python environment. Traditional programming concepts (operators, data structures, control structures, repetition, and user-defined functions) will be central to the learning objectives, but the concepts will be taught in the context of marketing and business analytics problems related to data management and visualization. In addition to high-level programming, the students will gain a foundational understanding of how data are organized and pulled from databases, including the querying process that turns raw data into the kinds of datasets that more advanced analytics tools leverage. The course involves hands-on tutorial assignments with practical pattern matching as well as less structured programming assignments, where the students are expected to write their own programs.

GBA468 expands and develops the students’ analytical toolkit to include prescriptive analytics methodologies for managerial decision-making. The coursework follows the FACt approach to business problem solving and will cover diverse applications in operational management, supply chain analytics, marketing, and finance. Modeling techniques covered include: Decision tree models Constrained optimization models Monte Carlo simulation The course will be taught primarily using Python.

MGC 461 is based on classical principles of argument and persuasion and current communication research that reveals the keys to having influence in a global business world. Its goal is to develop professional-level presentation skills, individually and in teams. The course is performance based; all class sessions require students to speak and interact extensively. Over two mini-semesters, students develop professional presentation skills through four individual and two team-based speaking assignments, for which they receive extensive, individualized feedback. They also learn a systematic process for assessing their own communication, and for giving feedback to others. At the end of the course students will have developed skills key to success in any field: the ability to adapt effectively to a given audience, and to express ideas with an appropriate combination of logic and feeling.

This course is designed to provide students with concepts and skills in managing data using traditional and cloud based systems. The course covers theory of database systems, data manipulation techniques, data integration and cleaning, and working with modern and advanced data architectures. The class covers both theory and hands on activities using Microsoft Access, SQL, Tableau and R. Microsoft Access, SQL, Tableau and R may be used extensively in class, for assignments and exams.

This course aims to train practitioners of analytics methods to construct, evaluate, and apply machine learning (ML) models in a variety of business applications using modern tools. The course covers programming tools for handling data, computational frameworks, cloud platforms, and an array of advanced ML algorithms. The course emphasizes hands-on work through class exercises, homework assignments, and projects. The course is self-contained, but basic programming skills are required.

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.

“Prepare for the dark while it is light.” In an era marked by significant economic uncertainty and rapid technological advancements, the ability to identify, measure, and mitigate financial risks has become indispensable for finance professionals. This course delves into the multifaceted landscape of financial risk management, covering a broad range of topics including market risk, credit risk, liquidity risk, and operational risk. Through a combination of theoretical frameworks, case studies, and practical applications, students will develop a deep understanding of the various types of risks faced by corporations and financial institutions, as well as the tools and methodologies employed to manage them effectively.
MBA Prerequisite: FIN 411

FIN 478 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.

This course covers the design and development of Generative AI-enhanced business applications. We will discuss a framework for integrating Gen AI tools and capabilities into business processes, tasks and workflows. Students will learn how to program with Gen AI tools and LLMs using Python and APIs, including methods for dynamic task automation and workflow coordination using agentic design. The course also covers the use of Retrieval-Augmented Generation (RAG) to access private knowledge bases built on organizational data. By the end of the course, students will be equipped with the technical and conceptual skills to design and build generative AI applications tailored to modern business needs.

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.

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 equips 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.

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 gives an overview of supply chain management in a wide variety of industries such as: groceries, style goods, consumer electronics and services. The impact of shifts from traditional channels to e-commerce will be emphasized. New initiatives introduced to address these new challenges, such as vendor managed inventory (VMI), variety postponement, cross docking, real options contracts and quick response, will be studied and applied both in class and assignments.

The topics treated in this course span a wide spectrum of issues, concepts, systems, and techniques for managing projects effectively in today’s complex business environment. Students are led through a complete project life cycle, from requirements analysis and project definition to start-up, reviews, and phaseout. Important techniques for controlling project costs, schedules, and performance are studied. The course employs a combination of lectures, case analyses, business/project simulations, videos, Internet resources, and group discussions to develop the conceptual understanding and operational skills needed for effective managerial role performance.