Free Tutorial – 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.
Who this course is for:
- Risk professionals that want a deep dive into risk management.
- Risk analysts that need a solid foundation in critical concepts and issues in risk management.
- Governance Risk and Compliance, Cybersecurity, National Security, Financial Risk, Medical Risk, and more.
- Risk modelers and Actuaries
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