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.
Data modeling has become a pervasive need in today's business environment. Often the volume of data you need to process goes beyond the capabilities of spreadsheet modeling. When this is the case, the statistical programming language R offers a powerful alternative. With R, you can avoid the cost of standalone statistical packages. Likewise, you don't need a huge investment in learning the structures required to use a more fully featured programming language.
In this course, you will work through the basic methods of predictive analytics, including generating descriptives, visualization, single and multiple regression, and logistic regression. The benefits of using R for logistic regression are significant, and these are explored in detail. When you have completed this course, you will have gained experience developing R code to solve novel problems in which basic predictive methods are required.
When faced with a large volume of unstructured data, the question quickly arises: what does this all mean? Techniques in machine learning offer the promise of a meaningful answer to that question. Unsupervised machine learning is a powerful tool that is being put to use in many disciplines. In this course, you'll experience machine learning through scripting in the statistical programming language R.
The course focuses on using unsupervised machine learning to bring coherence to unstructured data. Specifically, you'll use different methods to generate clusters within your data set when no dependent variable is specified. Using supervised machine learning approaches, you'll build and evaluate models that allow you to classify your data and understand the marginal impacts of each attribute. And you'll gain experience with powerful tools in R that allow you to efficiently evaluate competing models to find the one that gives you the most accurate results.
You are required to have completed the following course or have equivalent experience before taking this course:
- Predictive Analytics in R
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