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World Class Data-Driven Risk Analysis - Theory & Application
Rating: 4.4 out of 5(60 ratings)
1,499 students

World Class Data-Driven Risk Analysis - Theory & Application

Analyze and present risk with scientific rigor and improve stakeholder engagement. Build your skills, train your staff.
Created byRob Arnold
Last updated 7/2023
English
English [Auto],

What you'll 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.

Course content

2 sections • 8 lectures • 34m total length

  • Introduction1:53

    Learn the basic tools of risk management, including the risk formula, risk modeling, and risk registers. Explore data types and origins, confidence, and risk resolution to curb risk sprawl.

  • The Risk Formula3:26

    Explore the risk formula, linking likelihood and impact to assess risk with threats and assets as context. Focus on managing negative outcomes and the role of vulnerability and distributions.

  • Scenarios, Time and Risk Registers3:06

    Explore how risk scenarios model multiple threats and vulnerabilities, and manage time, forecast and validity periods within a risk register to handle explosive data growth.

  • Data Types, Origins and Confidence4:33
  • Risk Resolution3:30

    Define risk resolution through expanding scenarios and data to reveal a vivid risk landscape. Balance objective data with cautious confidence to avoid tainting risk pictures and misinformed decisions.

  • Summary and Resources1:39

Requirements

  • No experience necessary.
  • Start your journey into data driven risk analysis and modeling right here, right now!

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.

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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