Admissions & Curriculum

A key goal of GBA 471 is to generate insights from data to support business decision making. This course introduces basic techniques for data exploration and visualization, with an emphasis on understanding and reasoning with data and communicating data-driven results to a managerial audience. Generative AI will be used to uncover conjectures for further exploration of data sets and augment attainable insights from data.

This course introduces utilizing data and data analytics to inform decision-making in today’s business environment. Extracting information from data has become an integral part of modern business management, from Main Street to Wall Street to Silicon Valley. Generative AI will be used as a complementary tool throughout the course, as it can be paired with existing software and analytic techniques to greatly expand the scope of application. This course will de-mystify statistical learning and data analytics, enabling students to thrive in a competitive market for data-based decision-making.

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.

Marketing management is a complex business function that requires various skills for effective strategy formulation and marketing plan implementation. The key objectives of this course are (1) to provide you with a series of frameworks for identifying, analyzing, and solving marketing problems with an emphasis on analytic tools to achieve this aim, and (2) to enhance your ability to effectively communicate your analyses and solutions.

The Business Analytics Capstone project is an opportunity to bring all of the conceptual theories, analytical methodologies and technical skills from your coursework, as well as your creativity and intellectual enthusiasm, to bear on a real-world project that will directly impact your sponsor organization. There is the potential for students to develop a project proposal from their current job opportunities and use this as the subject for their capstone project.

Created in 1989, Python is one of the most popular programming languages in the world—attributable to its power, simplicity, and flexibility. Data analysts of all kinds (including business and marketing analysts, data scientists, management consultants, and product managers) can use Python- in combination with powerful, third-party packages including NumPy, Pandas, Matplotlib, and Seaborn—to import, explore, manipulate, analyze, and visualize data. By completing this course, you will be able to think like a programmer (with the practiced ability to break down complex problems into a series of smaller steps and pseudocode), write basic computer programs using Python (to implement your pseudocode and achieve your desired outcomes), and perform basic data analytics using Python (to import, explore, manipulate, analyze, and visualize data).
 

This class introduces students to AI models at the conceptual level and teaches how recent advances in AI can spur financial innovation for startups and corporations. The class relies on an analytical framework that combines financial frictions analysis and AI applications. Financial frictions provide opportunities for AI-driven FinTech solutions. The class covers the evolution of deep learning models in the last decade, discusses the cutting-edge AI applications, and helps students to identify new opportunities for value creation.

The objectives of this course are to understand the analytics environment, including people and organizational challenges, data, and the role of models, and to understand concepts and apply frameworks of data-driven decision-making for business decisions. GBA 473 covers marketing analytics, operational analytics, and business analytics. Generative AI is used throughout the course to enhance the process of discovery and the articulation of ideas.

GBA 474 covers the concepts and tools needed for the effective design of analytics, using non-system data and experiments to solve business problems. The course focuses on “soft” skills related to structuring complex problems, decision-making with imperfect information, and designing analysis to address analytics research questions. Generative AI is used throughout the course for both internal and customer-facing elements of analytics design and application.  It further reinforces and expands on “hard” skills related to data wrangling, descriptive, predictive, and causal analytics, unsupervised learning methods, and generative AI tools and applications.  

Students will learn to understand and utilize the best practices of storytelling using rhetoric, persuasion, influence strategy, analytics, data visualization, and Generative AI. Students will apply frameworks to convey business material to a variety of audiences, using standard office applications, data visualization platforms, and the latest Generative AI tools and techniques. Learn to apply storytelling concepts to connect information to insights, improving your ability to express your thinking and influence. 

Tech firms—from start-ups to industry leaders—face a range of ethical and policy challenges. Start-ups may face barriers to entry or regulations ill-suited to their innovation. Industry leaders contemplate antitrust considerations if they dominate a market. Moreover, tech firms of all sizes face ethical quandaries on issues related to AI. This course focuses on the skills needed to consider the policy and ethical environments of tech firms, and how these connect with firms’ market environments. 

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 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.