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.
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