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.
Faculty AuthorChris Anderson
Benefits to the Learner
- Calculate marginal value for a binary decision
- Determine optimal values for a repeating, sequential decision
- Build risk aversion into your model
- Calculate utility for a given decision
- Develop and use a Monte Carlo simulation
- Perform sensitivity analysis Use expected utility to accommodate risk
- Functional Managers
- Any professional that uses data to make business decisions
Applies Towards the Following Certificates
- Data Analytics 360 : Required Courses