I'm happy to share materials from any of these courses with other instructors. Just email me.
As generative AI was going mainstream, Olivier Toubia and I set out to build a course that tackles the business questions it raises: how these models work, where they fail, and where they create value, from prompting and evaluation to grounded assistants and agents. Students build a portfolio as they go, shipping working tools with AI coding agents in class, including their own deployed course copilot.
A customer-centric approach to marketing: understanding customers, designing the value proposition, and delivering and capturing value through the 4 Ps. Balances quantitative analysis with qualitative judgment through cases, group exercises, and projects.
Covers the same customer-centric marketing questions as the core course, with a much greater emphasis on analytics: using data and models to understand customers and to make and evaluate marketing decisions.
Using data, statistical models, and optimization to make better business decisions.
Starts with the building blocks (linear algebra, probability, and statistics), then models real business data with regression and factor analysis, mostly in Python.
Through Columbia Business School's Entrepreneurship and Competitiveness programs, which support owners of mid-sized companies in Latin America and Africa, I've advised: