Revenue forecasting is at the core of every financial plan—and the quality of your assumptions can shape business strategy. In this intermediate-level course, you’ll learn how to use two key forecasting frameworks: top-down and bottom-up. You’ll compare how each method builds projections from different starting points—market size versus operational data—and explore when to use one over the other. Then, you’ll apply scenario analysis to create a three-case model that captures optimistic, base, and pessimistic outcomes. Guided videos, readings, and spreadsheet-based labs walk you through building, testing, and visualizing forecasts that reveal both risk and opportunity. Along the way, you’ll practice explaining your results clearly and confidently to non-finance audiences.

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.