Python: Apply & Evaluate Sales Forecasting with Time Series

Build practical skills in sales forecasting by applying time series analysis in Python to real-world datasets. This hands-on course is designed for learners with foundational Python knowledge who want to develop and evaluate forecasting models using structured analytical techniques. You will begin by preparing raw time series data through preprocessing, feature engineering, and visualization. As you progress, you will identify trend, seasonality, and noise using time series decomposition to create high-quality data for forecasting. Next, you will train and evaluate SARIMA models using statistical metrics and compare forecasting performance across multiple datasets and categories. The course also introduces the Facebook Prophet library, where you will prepare data, generate forecasts, visualize predictions, and assess model accuracy using Prophet's built-in support for trends, seasonality, and holidays. By the end of the course, you will be able to preprocess time series data, engineer forecasting features, build and evaluate SARIMA and Prophet models, compare forecasting approaches, and visualize results to support data-driven sales forecasting decisions. If you want practical experience applying Python-based forecasting techniques from data preparation through model evaluation, this course provides a structured, project-focused learning experience.