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Python: Apply & Evaluate Sales Forecasting with Time Series

EDUCBA via Coursera

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Overview

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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.

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.