This course extends and deepens the material you learned in business analytics. We will apply these methods in more unstructured and diverse situations, introduce new analytics tools and methods (including Tableau Visualization, text mining, and random forests), and study a modern framework for overfitting reduction called regularization that underlies much of modern machine learning. This course does not require coding or knowledge beyond Business Analytics, but the mathematical sophistication level will be somewhat more advanced.
- Attendance at the first class is compulsory
- Completion of pre-class work is compulsory in order to remain in the class
- There will be a final exam
Associate Professor of Professional Practice; Director Center for Pricing and Revenue Management and Business Analytics Initiative
Daniel Guetta is Associate Professor of Professional Practice at Columbia Business School and Director of the Business School's Center for Pricing and Revenue Management. He is also Director of the Business Analytics Initiative at the Columbia Business School and Columbia Engineering. His research focuses on the ways companies can harness the power of data and analytics to drive value. He teaches...