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.

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.

Data Visualization: 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.

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 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 course explores the intersection of Generative AI (GenAI) with data analytics and business computing, focusing on how GenAI can be leveraged to solve complex business challenges in the era of big data. Through an exploration of fundamental business concepts such as the 'Job to Be Done', 'Value Proposition', and 'Product Market Fit', students will learn to integrate GenAI into various business scenarios effectively. The course is designed to equip students with the skills to critically evaluate the potential and limitations of GenAI, develop innovative solutions, and lead GenAI projects in diverse industries.

This course aims to equip you with a strategic perspective, conceptual understanding, and practical tools to add value when applying AI to business and finance. To this end, we introduce an end-to-end AI business solution process with practical use cases, data, and code examples. By the end of this course, you will have learned: (1) How to translate business problems and challenges into data science and machine learning problems (2) How to source, evaluate, and combine data for your business and modeling objectives (3) How to implement and carry end to end AI projects by using state of the art algorithms and tools (4) How to evaluate and iterate your solutions based on the results (5) How to be successful with AI solutions in a real-world environment.

Casual Inference course description: 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 aims to help students understand marketing innovations as well as develop and implement marketing innovations in the rapidly changing environment of technology, competition and consumer behavior. The focus is on how new information technology and data science have driven the innovations in marketing strategies, tactics and methodologies; and how they may change the future of marketing. The topics covered in the course include (but not limited to): (1) the theory of innovation and common approaches towards innovations in marketing, (2) marketing innovations and trends in consumer behavior, (3) innovations in marketing strategies and tactics with examples from U.S., China and around the world, (4) innovations in marketing research and analytics, (5) special topics on marketing innovations (e.g., big data and marketing innovations, mobile technology and marketing innovations, AI and marketing innovations, IoT and marketing innovations, smart retailing), etc.

This is a course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. The “social network” has captured popular imagination because of the spread of social media, however we have always been creatures of our networks—whether those networks involve family, villages, tribes, or Facebook. The topics we will cover include: social network structure and its effects on business and culture; understanding how the structural properties of networks help us understand social capital, power, ties and closure; the propagation through networks of information, fads and disease; power laws, network effects, and "rich-get-richer" phenomena; using networks for prediction; leveraging information networks for web search; networks and social revolutions, and the melding of economics, machine learning, and technology into new markets, such as "prediction markets" or markets for on-line advertisements.

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.