Reading Predictive Model Outputs for Non-Technical Professionals Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Understand the fundamentals of predictive models without requiring advanced technical knowledge.
• Interpret key outputs and results generated by predictive models.
• Understand probabilities, forecasts, confidence levels, and accuracy indicators.
• Distinguish between prediction, correlation, and causation.
• Assess whether a predictive model is appropriate for a specific business problem.
• Identify the key factors influencing predictive results.
• Compare predicted outcomes with actual results and identify deviations.
• Recognize indicators of weak model performance or unreliable predictions.
• Understand how data quality and bias can influence predictive outputs.
• Evaluate the risks and limitations associated with predictive decision-making.
• Ask effective questions when reviewing predictive model results with technical teams.
• Use predictive insights to support planning, performance management, and risk management.
• Strengthen managerial judgment when interpreting predictive outputs.
• Apply governance principles to the responsible use of predictive models.
• Translate predictive outputs into actionable business recommendations.

Who Should Attend

Learning Outcomes

• By the end of the course, participants will be able to:
• Explain the purpose of predictive models and their role in decision support.
• Interpret key predictive model outputs from a managerial perspective.
• Correctly interpret probabilities, forecasts, confidence levels, and uncertainty.
• Assess the quality and reliability of predictive results.
• Distinguish between reliable outputs and results requiring additional validation.
• Identify the key variables and factors influencing predictions.
• Compare predicted outcomes with actual performance and measure deviations.
• Recognize potential bias, uncertainty, and accuracy limitations.
• Understand the impact of data quality on predictive model outputs.
• Assess whether predictive results are suitable for a specific decision or business context.
• Communicate more effectively with data scientists, analysts, and technical teams.
• Use predictive insights to support planning, risk management, and performance improvement.
• Combine predictive insights with professional expertise and managerial judgment.
• Apply responsible-use and governance principles to predictive models.
• Convert predictive outputs into practical decisions and actionable recommendations.

Course Outline