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Forecasting in Practice

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Updated 05/2025
Learn how to design, automate, and monitor scalable forecasting pipelines in Python.
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Course Description

Learn how to build robust, scalable forecasting pipelines ready for production. This advanced course is designed for data scientists, machine learning engineers, and analytics professionals who want to move beyond experimentation and put time series forecasting into action. You'll work through the full production pipeline—from sourcing and preparing real-world data to training and evaluating multiple forecasting models using libraries like statsforecast and mlforecast. You'll then explore how to automate model training, deployment, and monitoring using tools such as MLflow and Airflow.Along the way, you'll apply forecasting principles to a real dataset from the U.S. Energy Information Administration and gain hands-on experience with API data extraction, exception handling, and performance logging.By the end of the course, you'll be equipped to design forecasting systems that can handle automation, scale, and long-term maintenance.Chapter 1 is available now as part of a prelaunch—start learning today while we finish the rest of the pipeline!

Prerequisites

Introduction to Apache Airflow in PythonIntroduction to MLflowIntroduction to Data Visualization with Plotly in PythonTime Series Analysis in Python
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