Demand and Supply Analytics I
Learn how to use data to develop insights and predictive capabilities to make better business decisions.
Modules/Weeks
Weekly Effort
Discipline
Format
Cost
Course Description
- Analyze demand and supply management challenges in various industries.
- Explore price optimization techniques, including static and dynamic pricing methods.
- Investigate market segmentation strategies and non-linear pricing models.
- Examine consumer choice modeling, customized pricing, and supply chain management basics.
Course Prerequisites
- Undergraduate-level knowledge in probability, statistics, linear algebra, and calculus
- Prior experience with basic programming concepts in a procedural programming
What You Will Learn
By the end of this course, learners will be able to:
Identify and evaluate business analytics opportunities to create business value.
Build models that support managerial and business decisions using analytical techniques.
Analyze case studies of organizations successfully deploying analytical methods.
Gain knowledge of basic analytical methods and their applications, including price response function, price elasticity, price optimization (static and dynamic), price differentiation, quantity-based revenue management, network revenue management, and overbooking.
Additionally, learners will study procedures like Schubert's Algorithm and DIRECT Algorithm, developing a strong foundation in revenue management analytics.
Course Outline
Module 1: Introduction
Module 2: Price optimization
Module 3: Price optimization in practice
Module 4: Pricing segmentation
Module 5: Quantity-based revenue management and overbooking machine learning 1
Module 6: Network revenue management and overbooking
Instructors
Daniel Guetta is an 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 Columbia Business School and Columbia Engineering. Guetta's research concentrates on using data and analytics to create value in businesses. He teaches courses in business analytics, data science, pricing, supply chain management, and technical tools such as Python and cloud computing. Guetta has written award-winning case studies in the area and co-authored "Python for MBAs," which was released in Fall 2020. Before joining Columbia's faculty, Guetta worked as a data scientist and engagement manager at Palantir Technologies. He holds a PhD in Operations Research from Columbia Business School, and completed his undergraduate studies in physics and mathematics at Cambridge and MIT.
Please note that there are no instructors or course assistants actively monitoring this course.