Overview

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In this intermediate-level specialization, you will build statistical and Machine Learning (ML)-based forecasts for business planning. You'll create time-series forecasts using Power BI, develop machine learning models with Azure AutoML, and evaluate forecast accuracy. You'll learn to communicate uncertainty and improve forecast reliability through iterative refinement. This course is for financial professionals with 1–3 years of experience, including analysts, who have a foundational understanding of financial principles and standard data tools. By the end of this course, you will be able to apply Power BI forecasting tools for time-series predictions with confidence intervals, build machine learning models for revenue prediction using Azure AutoML, evaluate forecast accuracy to optimize model performance, and present forecast scenarios with appropriate uncertainty communication. This course requires Power BI Desktop, which runs on Windows PCs or Macs with Parallels Desktop. A subscription to Azure ML is also required. Microsoft 365 online can be used as an alternative, though the desktop version is recommended for full feature access.

Syllabus

Power BI Forecasting: Build Time-Series Models

Use Power BI's built-in forecasting capabilities to create professional time-series predictions. You'll learn to configure forecast parameters, set appropriate confidence intervals, and generate forecasts for financial metrics.

Power BI Forecasting: Analyze Patterns

Dive deeper into understanding what drives your forecasts. You'll learn to interpret forecast components, identify seasonal patterns, and recognize trends that impact prediction accuracy.

Power BI Forecasting: Evaluate Accuracy

Learn to measure and improve forecast accuracy using statistical metrics. You'll calculate MAPE, understand prediction intervals, and iteratively refine parameters to achieve better predictions.

Power BI Forecasting: Professional Documentation

Transform your forecasts into professional deliverables. You'll learn to export forecast data, create assumption documentation, and build forecast packs that support financial planning processes.

ML Forecasting: Create AutoML Models

Launch your first machine learning forecast using Azure AutoML's no-code interface. You'll configure experiments, select algorithms, and generate predictions that capture complex patterns traditional methods miss.

ML Forecasting: Analyze Feature Importance

Unlock the "why" behind ML predictions through feature importance analysis. You'll interpret which variables most influence forecasts and translate technical outputs into business insights.

ML Forecasting: Evaluate Model Performance

Rigorously assess your ML model's performance against benchmarks. You'll calculate key metrics, compare to naive forecasts, and determine if ML adds sufficient value to justify complexity.

ML Forecasting: Refine and Document

Create new ML model versions through systematic experimentation and parameter optimization. You'll document each iteration and create deployment-ready documentation for ongoing use.

Project Module: Integrated Revenue Forecast Comparison

Apply your forecasting skills by creating both statistical and ML-based revenue forecasts, comparing their accuracy, and recommending the optimal approach for your organization's FP&A process.