Material I created as a TA is posted on the notes page.
Business Analytics 2 and Business Analytics 3 is an elective sequence I designed which serves an advanced AI sequence for MBA students at Columbia. It covers methods, as well as issues around the implementation of AI systems. We discuss the process of going from a business problem to an AI problem, and develop a holistic understanding of AI as a complex engineering system borne out of economic, social, and political forces.
Business Analytics 2, which I teach with Hong Namkoong, covers more "traditional" techniques, such as decisions trees and random forests. Business Analytics 3, covers modern techniques for high dimensional data (the Lasso) and unstructured data (deep learning).
I developed several tools to teach this class, including XLKitLearn, an Excel front-end for scikit-learn, and a fully trainable mini neural network entirely in Excel as part of the Binit case.
The syllabi for both sequences is here.
In 2021, I re-designed the entire Python curriculum at Columbia Business School. See this page for details.
This course provides students the opportunity to learn business analytics and data science by working on a set of company sponsored applied projects. Students teams of 5-6 people, with 3-4 MBA students and 1-2 engineering (SEAs) students, will work hand in hand with the instructors and company representatives to achieve company goals through the practical application of data analytics.
As part of the class, students create a blog post describing their project - these are compiled here. The Columbia Bizcast also ran an episode about the class.
Are you a company interested in participating in the class? All the details are here.
Business Analytics is part of Columbia Business School's core curriculum, and introduces our students to a broad range of topics - logistic regression, k-nearest-neighbors, optimization, simulation, difference in differences, and more.
This class combines Columbia Business School's core Managerial Statistics and Business Analytics classes, but teaches them in Python rather than Excel. It is a required class for all students in Columbia's MBAxMS program, and Columbia's MSBA program.
The Analytics Advantage is a survey class taught by six different faculty in the DRO division, each on a topic of their expertise. The class focuses on how analytics have generated value in a broad range of industries. I launched and coordinated the class in the Spring of 2020. I teach one lecture in the class - initially, on ridesharing apps, and since the Spring of 2024 on advanced optimization.
Operations Management is part of Columbia Business School's core curriculum, and introduces our students to a broad range of topics - queuing, supply chain management, the newsvendor, process analysis, market design, fulfillment, and more.
This class focused on the strategy of AI implementation - when a company decides to focus on AI, there are usually hundreds of potential places they could start. Which should they pick, and what are the first steps they should take on their journey?
The syllabus for the class is available here.
I designed this class for our engineering masters students to cover the basics of demand and supply analytics. The course covered everything from static pricing, to dynamic pricing (price-based and quantity-based), to customized pricing, to overbooking, to dynamic pricing, to conjoint analysis, to qualitative pricing strategies.
Due to changes in staffing, I no longer teach this class. I do, however, teach an EdX version of the class, administered by the school of engineering.