Forecast Business Metrics: Uncover Value Drivers
About this course
In this short, practical course, you’ll learn how to use supervised learning to forecast key business metrics and uncover the drivers that shape performance. Through hands-on exercises in Python, you’ll build and tune regression and gradient-boosted models to predict outcomes such as next-quarter EBITDA. Then, you’ll apply explainable AI techniques, including SHAP and feature importance, to translate model outputs into clear, actionable business insights. By the end of the course, you’ll be able to evaluate forecast accuracy, identify which variables truly drive results, and communicate your findings in simple, stakeholder-ready language. Designed for analysts and data professionals, this course helps you connect data science methods to real-world business forecasting and decision-making.
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What you'll learn
- forecast key business metrics
- build regression models in Python
- apply gradient-boosting techniques
- interpret model results using SHAP
- communicate findings effectively to stakeholders
Course objectives
- teach practical applications of supervised learning
- connect data science methods with business forecasting
- improve decision-making through data insights
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