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.


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:

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  • 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.




The Risk Formula
Scenarios, Time and Risk Registers
Data Types, Origins and Confidence
Risk Resolution
Summary and Resources

Visualization, Expected Loss, & Modeling

Visualization, Expected Loss, & Modeling
Python Model Walk Through