Practical Python for Time Series Analysis
With Jesus Lopez
Liked by 266 users
Duration: 4h 55m
Skill level: Intermediate
Released: 12/5/2025
Course details
Time series data drives critical decisions in finance, energy, and economics—but requires specialized skills. Full-stack data consultant and solutions architect Jesús López guides you through practical Python techniques, combining software engineering and statistical analysis using pandas, statsmodels, and plotly.
Work with real datasets from the Federal Reserve (FRED), PJM energy markets, and financial data to master essential skills: joining temporal datasets with different frequencies, aggregating data across time periods with groupby, creating pivot tables for comparisons, and resampling to match analysis needs.
Learn regression analysis for time series: fit OLS models, calculate R-squared from scratch to understand model quality, diagnose autocorrelation with Durbin-Watson tests, and apply HAC standard errors to correct inflated significance. Discover how temporal discretization and regime-specific modeling dramatically improve explanatory power when economic relationships change across periods.
Every technique is taught through hands-on examples with energy generation, economic indicators, and stock data—giving you immediately applicable skills for your temporal datasets.
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.
-
Showcase on your LinkedIn profile under “Licenses and Certificate” section
-
Download or print out as PDF to share with others
-
Share as image online to demonstrate your skill
Meet the instructor
Learner reviews
Contents
What’s included
- Learn on the go Access on tablet and phone
Similar courses
Download courses
Use your iOS or Android LinkedIn Learning app, and watch courses on your mobile device without an internet connection.