Chris Anderson is a professor at the Cornell Nolan School of Hotel Administration. Prior to his appointment in 2006, he was on the faculty at the Ivey School of Business in London, Ontario, Canada. Professor Anderson’s main research focus is on revenue management and service pricing. He actively works in the application and development of revenue management across numerous industry types, including hotels, airlines, and rental car and tour companies, as well as numerous consumer packaged goods and financial services firms. Professor Anderson’s research has been funded by numerous governmental agencies and industrial partners. He serves on the editorial board of the Journal of Revenue and Pricing Management and is the regional editor for the International Journal of Revenue Management. At the Nolan School of Hotel Administration, Professor Anderson teaches courses in revenue management and service operations management.
Sometimes the problem you need to solve involves amounts of data or numbers of decisions that go well beyond the capabilities of spreadsheets. You can work around these limitations by replicating spreadsheet methods of simulation and optimization in the script-based programming environment in R. The use of R carries the benefits of flexibility, automation, and expanded set of tools and algorithms.
In this course, you will work through the development and implementation of Monte Carlo simulations. You'll become familiar with the R functions most commonly used for this purpose. You'll also translate optimization problems that have been defined outside R to a form that supports computational solutions in R. You'll work with both linear and nonlinear solution methods.
It is recommended that students have a background in data analytics especially with optimization, modeling, and monte carlo simulations, in addition to a familiarity with programming syntax.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Predictive Analytics in R
- Clustering, Classification, and Machine Learning in R
Key Course Takeaways
- Simulate future outcomes for an index constructed from multiple sources of data
- Make a decision based on a simulation model
- Reduce decision uncertainty by visualizing and interpreting relative importance of a large number of independent variables
- Obtain an optimized result in R
- Interpret shadow prices from an R-based linear optimization
- Interpret shadow prices from an R-based quadratic (nonlinear) optimization
- Obtain an optimized result for a nonlinear, non-quadratic model
How It Works
Who Should Enroll
- Data scientists
- Functional managers
- Any professional that uses data to make business decisions