Persuasive communication is a vital component to many aspects of business life. This course introduces the basics of communication strategy and persuasion: audience analysis, communicator credibility, and message construction and delivery. Written and oral presentation assignments derive from cases that focus on communication strategy. Students receive feedback to improve presentation effectiveness. Additional coaching is available for students who want to work on professional written communication.

This course is designed to achieve an understanding of fundamental notions of data presentation and data analysis and to use statistical thinking in the context of business problems. The course deals with modern methods of data exploration (designed to reveal unusual or problematic aspects of databases), the uses and abuses of the basic techniques of inference, and the use of regression as a tool for management and for financial analysis.

This course provides an introduction to programming languages and to the software design methods. The programming language of choice is Python. However, the course will introduce the students to the fundamental programming concepts appearing in various other programming languages, including Java and C, that go well beyond the specifics of Python. Upon completion of this course, the students will be able to acquire practical programming skills in Python and understand the principles of structured software development. They will also understand the principles of designing large software systems and what it takes to plan, analyze, design, implement and support large Information Systems throughout their entire System Development Lifecycle.

With businesses generating and capturing increasing amounts of data, the ability to interpret and present insights in a persuasive way is more crucial now than ever before. Visualizing Data shows you how to make sense of your data, present clear evidence of your findings, and tell engaging stories all through data graphics.

Stochastic modeling and simulation plays an important role in making decisions related to the overall health of the firm. In this course we will develop both spreadsheet models and discrete event simulations to help firms predict the range of likely future scenarios that they may face and also to analyze the effects of decisions that they make. Data analysis on simulation outputs will also be taught. Applications covered in the course include the analysis of several investment opportunities, project management, inventory theory and the analysis of queueing models.

This course serves as an introduction to operations, viewed from the perspective of the general manager, rather than from that of the operations specialist. The coverage is very selective; the course concentrates on a small number of themes from the areas of operations management and information technology that have emerged as the central building blocks of world-class operations. It also presents a sample of key tools and techniques that have proven extremely useful. The topics covered are equally relevant to the manufacturing and service sectors.

This course aims to be an introduction to applied machine learning, deep learning and their applications in business, especially in the area of marketing, finance, operations etc. The course will cover the current state of the art machine learning algorithms, such as ensemble learning for structured data, CNN (convolution neural networks) for computer vision problems, transformer models (BERT, GPT) for natural language processing problems, GANs (generative adversarial network) for style transfer and synthetic data generation, recommender system etc. While discussing the above topics, this course focuses more on an intuitive understanding of the concepts, hands-on practice, and business applications than the theoretical foundation behind these methods.

This 14-week course explores the transformative potential of Generative AI (GenAI) in business contexts through a blend of theoretical concepts and hands-on application. Designed for students interested in the intersection of technology and business innovation, the course follows a biweekly project cycle where theoretical / conceptual foundations alternate with practical implementation sessions. 

Students will progressively build skills in developing AI-powered solutions, starting with basic AI assistants and advancing to complex multimodal systems and even autonomous agents. The course covers essential business frameworks like "Job To Be Done" and Value Proposition Design, while developing technical capabilities in prompt engineering, data analytics, sentiment analysis, multimodal content generation, and workflow automation. 

Each two-week cycle culminates in a practical project deliverable, building toward a comprehensive final innovation challenge where teams will develop a practical real-world GenAI business solution in a given area. 

By the end of the course, students will have developed both the conceptual understanding and practical skills needed to lead GenAI initiatives in various business contexts, while addressing important considerations around ethics, data quality, and responsible implementation.

This course teaches statistical and experimental methods and tools for understanding the relationship between cause and effect. Topics include treatment effects, experiment design, A/B testing, instrumental variables, propensity score matching, panel data, difference-in-differences, and regression discontinuity.

This course trains students to turn real-world problems into mathematical optimization models and to use such models to make better managerial decisions. Students will be introduced to the theory, algorithms, and applications of optimization. It covers both deterministic and stochastic optimization models. Students will develop and solve various models with optimization software. The application areas are diverse and they originate from problems in finance, marketing and operations.

Topics covered:

• Linear and linear integer programming
• Network Flow
• Nonlinear programming
• Duality and sensitivity analysis
• Stochastic programming
• Robust optimization
• Dynamic programming

The Capstone Project is a for-credit experiential learning course that integrates and weaves together concepts learned from the other constituent courses that comprise the curriculum and links them to practical applications. In small groups starting with pre-work during the Fall semester, students will work together to solve cases presented by companies.

This course introduces students to the strategic and practical applications of artificial intelligence (AI) in marketing analytics. Grounded in core marketing challenges, the course examines how AI tools—across descriptive, predictive, prescriptive, and generative categories—can enhance marketing strategies. Students will gain hands-on experience using state-of-the-art AI technologies to solve real-world marketing problems, such as customer segmentation, social listening, churn prediction, A/B testing, and branded content generation.

Beyond technical capabilities, the course emphasizes the strategic choices marketing leaders face when adopting AI systems, including questions of implementation, interpretability, and cross-functional alignment. Students will analyze how AI-generated outputs are perceived by consumers and managers, and evaluate their implications for brand equity, customer trust, and organizational performance. Special attention is given to generative AI's impact on creativity and marketing workflows.

The course also engages students in the ethical, regulatory, and societal dimensions of AI in marketing—addressing challenges such as algorithmic bias, fairness, explainability, and governance. By the end of the course, students will be equipped to lead AI-driven marketing initiatives with both strategic insight and critical responsibility.

This course provides a comprehensive introduction to the foundational concepts of Artificial Intelligence (AI) and their practical applications in business. Students will delve into key AI domains—including machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Combining lectures, hands-on labs, and real-world case studies, the course equips students with the skills to design, implement, and critically evaluate AI-driven solutions to complex business challenges. By the end of the course, students will be able to bridge the gap between technical capabilities and strategic business objectives, enabling impactful and responsible AI integration in organizational settings.

Professional Responsibility and Leadership (PRL) is an interdisciplinary course that builds on prior coursework students have completed. In PRL, students pursue the following learning objectives: 1) to reflect on why they are embarking on a career in business, and how they intend to act as business professionals; 2) to think systematically about the risks and sources of resilience relevant to their professional lives; 3) to cultivate the habit of engaging in reflective dialogue with diverse stakeholders. The basic format of the course is a discussion seminar, drawing from three different sources: 1) the students’ own personal experiences and values; 2) expert insights drawn from a variety of academic disciplines (including philosophy, literature, history, and art, as well as the natural and social sciences); and 3) relevant contemporary and historical business cases. PRL focuses primarily on the students’ own interests, refining them through dialogue and in reference to expert sources. 

Professional Responsibility and Leadership (PRL) is an interdisciplinary course that builds on prior coursework students have completed. In PRL, students pursue the following learning objectives: 1) to reflect on why they are embarking on a career in business, and how they intend to act as business professionals; 2) to think systematically about the risks and sources of resilience relevant to their professional lives; 3) to cultivate the habit of engaging in reflective dialogue with diverse stakeholders. The basic format of the course is a discussion seminar, drawing from three different sources: 1) the students’ own personal experiences and values; 2) expert insights drawn from a variety of academic disciplines (including philosophy, literature, history, and art, as well as the natural and social sciences); and 3) relevant contemporary and historical business cases. PRL focuses primarily on the students’ own interests, refining them through dialogue and in reference to expert sources.