Advanced Visualization in Jupyter Notebooks
summary
Live demonstration of interactive visualization inside Jupyter builds reusable notebooks that load real weather and crime CSVs, compute pandas rolling moving averages, draw scatter and time-series side-by-side, apply smoothing and simple linear regression, and compute correlations (examples show ~0.3 and ~0.15). Code uses notebook widgets and web-friendly plots to filter by location and time, highlights peaks and anomalies, and produces exploratory dashboards that run in-session with immediate, reproducible output.
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chapters (7)
- 0:00Introduction to talk
- 4:34Why visualization matters
- 10:04Time series plots demo
- 15:31Scatter plots and relations
- 22:37Advanced techniques overview
- 26:12Open source tools
- 30:46Q&A and conclusion
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