Analyze and present risk with scientific rigor and improve stakeholder engagement. Build your skills, train your staff.
What you will learn
The Risk Formula – The foundation of risk modeling.
Risk Scenarios and Risk Registers – Effective data capture and organization.
Data Types and Data Sources – How to spot data issues.
Confidence and Certainty – Accuracy and precision in risk management.
Risk Resolution – How to engage stakeholders, the right way.
Risk vs. Expected Loss – Know the difference and why it matters.
Expected vs. Actual Loss – Theory meets the real world.
Data Types and Data Origins – Interpret data and avoid hidden dangers.
Modeling Risk with Python – Virtual experiments to confirm or refute a risk hypothesis.
Description
After completing this course, risk professionals will be able to identify and improve existing data-driven risk management programs and improve communication with decision making stakeholders. Budding risk analysts will get a solid education in the most overlooked and misunderstood elements of data-driven risk management. The course culminates in a brief introduction to modeling risk using Python notebooks — source code included.
Applications: Supply Chain Risk, Cyber Risk, Medical & Health Risk, Insurance, and Business Risk.
Risk Analysis – Part One
Introduces the world class instructor, the basic tools of risk management, and the macro-scale problems risk practitioners face. Topics include:
- The Risk Formula as a wireframe for risk modeling as well as commonly encountered variations of the formula.
- Risk Scenarios and Risk Registers as basic organizational and data capture techniques.
- Time is an implied and often overlooked element of risk analysis.
- Data Types and Data Origin which are the foundation of data interpretation.
- Confidence and Certainty that characterize overlooked issues with accuracy and precision.And finally,
- Risk Resolution is introduced to explain and communicate the problem of risk sprawl.
Risk Analysis – Part Two
Covers more advanced fundamentals, such as:
- Risk vs. Expected Loss
- Expected vs. Actual Loss
- Data Types and Data Sources – Understand and interpret data.
- Heat Maps
It also introduces risk modeling in Python and a walk though of the course code.
Content