Chris Anderson is a professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, he was on faculty at the Ivey School of Business in London, Ontario, Canada. His main research focus is on revenue management (RM) and service pricing. He actively works with industry, across numerous industry types, in the application and development of RM, having worked with a variety of hotels, airlines, rental car and tour companies, as well as numerous consumer packaged goods and financial services firms. 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 School of Hotel Administration, he teaches courses in revenue management and service operations management.
Overview and Courses
Data analytics is among today’s fastest-growing and highest-paid professions, as organizations increasingly rely on data to drive strategic business decisions.
This certificate is designed to expand your analytical capabilities and take your strategic decision making to the next level. These courses will delve into more advanced techniques in prescriptive analytics including optimization and modeling. In these additional targeted courses, you’ll learn how prescriptive analytics allows you to not only predict what will happen, but suggest actions for achieving predicted outcomes based on the interdependent effects of multiple decisions. Through hands-on exercises and video instruction from Cornell University faculty expert Chris Anderson, you’ll learn how to combine data visualization, predictive models, and prescriptive analytics to increase the accuracy of your predictions and make better, more agile business decisions.
Whether you’re an analyst or a senior executive, this certificate is designed to enhance functional literacy in critical business analytics and take your decision making to the next level. You’ll learn scientific methods for data analysis and visualization and gain a more complete understanding of risk and probability, using statistical models to optimize outcomes for complex—and often simultaneous—business decisions.
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.
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.
The course Understanding and Visualizing Data is required to be completed prior to starting this course.Decision making is never as simple as we would like it to be, since rarely does a single factor alone predict an outcome. In a competitive business environment, not taking this uncertainty into account has serious costs. In this course, you'll use foundations in probability to describe risk mathematically and incorporate those calculations into your decisions so you can take them to the next level. Working through increasingly complex modeling situations, you will learn to use estimates of probable future outcomes for Go/No-Go decisions and to run a Monte Carlo simulation allowing you to examine outcomes that vary based on multiple, interdependent decisions.
The courses Understanding and Visualizing Data, Implementing Scientific Decision Making, and Using Predictive Data Analysis are required to be completed prior to starting this course.
In business, we don’t often have the luxury of making one decision at a time; instead, we usually face multiple decisions at once, in highly complex situations where each decision has potentially far-reaching impacts. In this environment, professionals need a robust, quantifiable understanding of these ripple effects in order to meet business objectives and raise the odds of decision-making success. In this course, you will create and use data models for optimizing decision making in situations where resources are constrained—and two or more decisions whose consequences interact must be made simultaneously.
These courses are required to be completed prior to starting this course:
- Understanding and Visualizing Data
- Implementing Scientific Decision Making
- Using Predictive Data Analysis - Modeling Uncertainty and Risk
How It Works
Faculty Author
- Create and interpret statistical summaries and data visualizations that
support understanding and guide decision making. - Use data and key performance indicators to build a dashboard that uses visuals to improve your understanding of complex business situations.
- Formulate a business question as a scientific hypothesis that can be tested using statistical methods.
- Create and validate regression models that can be used to determine the effect of attributes on a decision and predict likely outcomes.
- Use data to describe and reduce uncertainty in decision making.
- Incorporate uncertainty and risk into decision models.
- Use data models to predict outcomes in complex situations with multiple, simultaneous decisions.
Download a Brochure
Not ready to enroll but want to learn more? Download the certificate brochure to review program details.
- Data Analytics 360 Certificate from Cornell SC Johnson College of Business
- 75 Professional Development Hours (7.5 CEUs)
- 26 Professional Development Units (PDUs) toward PMI recertification
Who Should Enroll
- Analysts
- Functional managers
- Executives
- Consultants
- Any professional that uses data to make business decisions
{Anytime, anywhere.}