I roughly divide my classes into three categories
I always welcome opportunities to collaborate on classes and teaching material. In addition, you are welcome to use any of the material on this website for your own teaching, provided you reach out to let me know you're using it (I'm also happy to answer questions and provide support).
MBA and EMBA classes
These classes are focused on preparing tomorrow's managers to navigate an increasingly data driven world. This includes a whole gamut of classes, from more technical ones which teach the language of data science, to more qualitative ones discussing the broader landscape of analytics in industry, challenges inherent in managing and working with engineers, and basic analytic literacy.
I currently teach the following classes at Columbia
- Business Analytics is part of the Columbia Business School core class, and introduces our students to the fundamentals of Business Analytics. The course is designed and maintained by the entire DRO division. Latest syllabus here.
- Business Analytics 2 is a follow-on to Business Analytics that I designed and taught for the first time in the Fall of 2018; I designed XLKitLearn in parallel, and use it extensively in the class. Within 6 months, this class grew to become a "staple elective" - I teach 6-7 sections of the class per year, and demand far outstrips supply. Latest syllabus here, and a more detailed outline here.
- Analytics in Action is a project-based class I teach with Brett Martin. We bring groups of roughly 3 MBAs and 2 engineers together, and pair them with companies with whom they work on data-driven projects under our supervision. I give short vignettes on Data Science at the start of every lecture, but most of the class comprises office hours working on specific projects. Here is a matplotlib cheat-sheet I produced for this class.
- The Analytics Advantage is a class I coordinate, but do not teach in. The class focuses on how analytics have generated value in a broad range of industries. Each class is taught by a different faculty member with specific subject matter expertise and focuses on one specific industry and on how it has been transformed through the use of analytics. Syllabus here.
I also teach a number of executive education seminars on business analytics (the material for this class is a joint effort with my colleagues which prevents from more sharing it) and pricing (material available on demand).
I have TAed the following classes in the past
- Managerial Statistics (Columbia) introduces students to the rudiments of frequentist statistics, including probability, normal distributions, confidence intervals and hypothesis tests. I prepared a number of "cheat sheets" for us in the course on confidence intervals, hypothesis tests, understanding Excel's regression output, and looking up t-values and z-values in statistical tables.
- Decision Models (Columbia) introduced MBA students to optimization and simulation using Risk Solver Platform in Excel (this is since been replaced by Business Analytics, above). I wrote some review sessions to help clarify concepts and provide some practice; here they are, with solutions (Excel files require Risk Solver platform) - review 1 (pdf, xls), 2 (pdf, xls), 3 (pdf, xls), 4 (pdf, xls), 5 (pdf, xls). I also prepared a presentation to review confidence intervals.
Engineering and PhD classes
These classes are focused on teaching more advanced analytics to students with a solid background in deterministic and stochastic modeling. The focus here is on cutting-edge analytics as well as on the challenges inherently involved in separating the hype from the substance and applying these techniques in the real world.
I currently teach the following classes at Columbia
- Business Analytics is the engineering equivalent to the Business Analytics core class described above. Whereas the MBA class is a 1.5 credit, half-semester class, this class is a 3 credit full-semester class. The additional lectures are spent covering the basics of the R programming language, and covering more advanced topics. Syllabus here.
- Demand Analytics is a class that covers the fundamentals of demand analytics, including pricing and consumer choice models. Syllabus here. I wrote a number of cases for this class, some of which are listed here, and some which currently only live on slides for now.
- Analytics in Action see the MBA and EMBA section above.
I TAed the following classes in the past
- Convex Optimization (Columbia) was a class I TAed for Prof Garud Iyengar. I designed a number of review sessions for the class; [contents](/files/teaching/convex_opt/Read me.txt), reviews 1, 2, 3, 4, 5, 7, and midterm review.
- Part IA Physics (University of Cambridge)
- Course 8.02 (Electromagnetism) (MIT)
- Course 8.01 (Classical Mechanics) (MIT)
I have taught the following classes
- Demand and Supply Analytics on EdX is an online version of my Demand Analytics class (see Engineering and PhD classes), with the addition of some supply-chain content. This class has thus far been taken by thousands of students with very positive reviews. The class is available on EdX here.
- Python for Managers with Emeritus, co-taught with Mattan Griffel. This class is designed to provide an introduction to Python for MBAs who have never coded before. The first part of the class (taught by Mattan) focuses on Python fundamentals such as data types, control structures, functions, and packages. The second part of the class (which I teach) focuses on Pandas for data analysis, including a section of web scraping and the use of APIs. See here for the course page on Columbia's website.