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.
Important business decisions require justification, and while we often have data that can help us make those decisions, the skill with which we analyze the data can make the difference between a good and bad outcome. This course, developed by Professor Chris Anderson, is designed to move learners beyond making decisions focused solely on averages. In this course, you will develop a working familiarity with the grounding principles of data analysis. You will learn to derive the greatest benefit possible from the data available to you while ensuring that the conclusions you draw remain valid. You will apply a decision-making framework within which you'll interact with the data to achieve the best outcome.
This course includes valuable tools and help sheets for data handlers along with the insight and perspective you need as a data consumer. While this course is not a replacement for a full-length statistics course, you will have a basic grounding in many statistics concepts by the time the course is over. You should be able to complete this course without any prior knowledge of statistics.
Project Management Institute (PMI®) Continuing Certification: Participants who successfully complete this course will receive 6 Professional Development Units (PDUs) from PMI®. Please contact PMI ® for details about professional project management certification or recertification. PMI is a registered mark of the Project Management Institute, Inc.
Summary statistics are one way to forecast uncertain outcomes, and the statistical results can be used to make decisions or guide strategy. Since summary statistics are based on a data sample, they typically inform intuitive decision-making. That is, the model requires interpretation which relies on the business intuition of the person using it.
You'll learn how to examine sample data scientifically to limit any generalizations to only the patterns that have the strongest statistical support. As always, intuition and business knowledge play an important role in the process, but this course will prepare you to apply a level of scientific rigor that will lead to better results.
You are required to have completed the following course or have equivalent experience before taking this course:
- Understanding and Visualizing Data
The sheer variety of sources and types of data that can aid in decision making are almost overwhelming. The key to making good use of the data lies in knowing what specifically to pay attention to, understanding the relationships and variables among the data, and making the right connections.
Experience is essential to knowing and making educated guesses about what to pay attention to. Familiarity with statistical methods will provide you with a significant advantage over relying on gut instinct alone.
In this course you will learn to identify uncertainty in a business decision, and to choose variables that help reduce uncertainty. By the end of this course, you will have a robust decision model that you can use to make predictions related to your decision. Along the way, you will clarify and enhance your understanding of the factors that influence possible outcomes from the decision.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Understanding and Visualizing Data
- Implementing Scientific Decision Making