Python for Time Series Forecasting
With Jesus Lopez
Liked by 396 users
Duration: 4h 19m
Skill level: Intermediate
Released: 7/17/2025
Course details
Learn practical time series forecasting with Python using real-world datasets from energy (EIA – U.S. Energy Information Administration) and economics (FRED – Federal Reserve Economic Data).
Build skills step by step, from loading and preprocessing time series data to decomposing trends and seasonality, visualizing patterns with Plotly, and applying forecasting models like ARIMA, SARIMA, exponential smoothing, and Prophet. Learn to evaluate model performance using error metrics and cross-validation techniques like walk-forward validation.
The course emphasizes hands-on exercises in a GitHub Codespaces environment, so you can immediately apply what you learn to your own datasets. Whether you’re working with sales, energy, or financial data, you’ll gain the skills to generate accurate, interpretable forecasts that drive real-world decisions.
Skills you’ll gain
Earn a sharable certificate
Share what you’ve learned, and be a standout professional in your desired industry with a certificate showcasing your knowledge gained from the course.
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Showcase on your LinkedIn profile under “Licenses and Certificate” section
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Download or print out as PDF to share with others
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Share as image online to demonstrate your skill
Meet the instructor
Learner reviews
Contents
What’s included
- Test your knowledge 15 quizzes
- Learn on the go Access on tablet and phone
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