World Class Data-Driven Risk Analysis – Theory & Application

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.