{"id":11353,"date":"2026-08-10T10:31:03","date_gmt":"2026-08-10T10:31:03","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-python-for-time-series-forecasting-2025-7\/"},"modified":"2026-08-10T10:31:03","modified_gmt":"2026-08-10T10:31:03","slug":"linkedin-python-for-time-series-forecasting-2025-7","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-python-for-time-series-forecasting-2025-7\/","title":{"rendered":"LinkedIn \u2013 Python for Time Series Forecasting 2025-7"},"content":{"rendered":"
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Description<\/h2>\n

Python for Time Series Forecasting is a course on analyzing, modeling, and forecasting time series data using Python, published by LinkedIn Online Academy. This is a comprehensive course that equips individuals with the skills to analyze, model, and forecast time series data using Python. Designed for data scientists, analysts, and developers, this course explores the fundamental and advanced techniques needed to effectively forecast time series in industries such as finance, energy, retail, and more. Individuals gain hands-on experience through hands-on projects and real-world datasets.<\/p>\n

Learn practical time series forecasting with Python using real-world datasets from the Energy (EIA \u2013 U.S. Energy Information Administration) and the Economy (FRED \u2013 Federal Reserve Economic Data). Learn step-by-step skills, from loading and preprocessing time series data to analyzing trends and seasonality, visualizing patterns with Plotly, and applying forecasting models such as ARIMA, SARIMA, exponential smoothing, and Prophet. Learn to evaluate model performance using error measures and cross-validation techniques such as walk-forward validation. The course emphasizes hands-on exercises in the GitHub Codespaces environment, so you can immediately apply what you learn to your own datasets. Whether you\u2019re working with sales, energy, or financial data, you\u2019ll gain the skills you need to produce accurate, interpretable forecasts that drive real-world decisions.<\/p>\n

What you will learn in Python for Time Series Forecasting:<\/h3>\n