Python: Apply & Evaluate Sales Forecasting with Time Series

Overview

This practical, hands-on course equips learners with the skills to analyze, build, and evaluate sales forecasting models using advanced time series techniques in Python. Designed for learners with foundational Python skills, the course progresses from preprocessing raw time series data to implementing complex forecasting models including SARIMA and Facebook Prophet. Learners begin by preparing data through structured preprocessing, feature engineering, and time series decomposition to uncover patterns and trends. The course then guides learners in training and statistically evaluating SARIMA models, validating model performance, and visualizing predictions. Through real-world comparisons of multiple datasets and categories, learners explore advanced model evaluation methods. The second half of the course focuses on the Prophet library, where learners will construct, visualize, and critically assess forecasts using Prophet’s intuitive capabilities for modeling trend, seasonality, and holidays. By the end of the course, learners will be able to apply statistical reasoning, build robust forecasting models, compare prediction strategies, and visualize results to support data-driven sales decisions.

Syllabus

Data Preparation and Visualization

This module introduces learners to the foundational steps of time series analysis for sales forecasting, including data preprocessing, feature engineering, and visualization. Through hands-on demonstrations and practical examples, learners will clean, structure, and transform raw time series data, create meaningful features such as lags and time components, and visualize essential components like trend and seasonality. The focus is on preparing data effectively to ensure high-quality input for modeling and forecasting in future modules.

Forecasting Models and Evaluation

This module guides learners through the process of building, evaluating, and comparing forecasting models using Python. It begins with the training and statistical evaluation of SARIMA models, followed by a practical comparison of time series forecasts across multiple datasets. The module then introduces the Prophet library, showing how to install, configure, and implement forecasts using Prophet’s built-in support for trends, seasonality, and holidays. Learners will visualize predictions and assess model accuracy to inform data-driven decisions.