Learn how to prepare demand data for AI forecasting through data cleaning, feature engineering, and data leakage prevention. This course is designed for data analysts, forecasting practitioners, and learners interested in building reliable forecasting datasets.

You'll explore how to identify and resolve data quality issues, including missing values, outliers, duplicates, and inconsistent data types. Learn to apply numerical transformations, scaling techniques, and categorical encoding methods to prepare structured data for predictive modeling. Discover how feature engineering improves forecasting datasets by constructing new variables, applying domain knowledge, and evaluating dimensionality reduction and feature selection techniques. Explore specialized data preparation methods, including time-series lags and rolling windows. You'll also examine chronological data splitting, data leakage risks, feature stores, and reproducible pipelines to support model training and production workflows. By the end of the course, you'll be able to clean demand data, transform and encode variables, construct and select predictive features, identify leakage risks, and organize forecast-ready datasets. What makes this course distinctive is its end-to-end focus on demand data preparation, connecting data quality, feature engineering, and leakage prevention within a practical AI forecasting workflow.