SDG Analytics uses public Sustainable Development Goal data to teach responsible data literacy, visualization, and multi-indicator interpretation.
Why SDG analytics matters
Public sustainability data is useful only when context, data quality, uncertainty, and responsible interpretation are visible. This topic keeps the emphasis on learning and transparent analysis rather than overclaiming what one chart can prove.
Data workflow
- Orientation: understand the goal, indicator, country, year, and data source.
- Data science method: load, clean, document, and inspect data before modeling or visualization.
- Visualization: make patterns readable without hiding limitations.
- Multi-indicator analysis: compare indicators carefully and avoid unsupported causal claims.
Featured learning path
- Watch: SDG Analytics playlist
- Inspect the GitHub proof artifact: data_science_on_SDGs
Future package placeholder
Future package: SDG Analytics Starter Kit. Status: planned / draft for review. It will only be linked as a product after Frank reviews the content, delivery path, and disclaimer.
Continue from here
- Watch: SDG Analytics playlist
- Inspect: data_science_on_SDGs
- Future resource: SDG Analytics Starter Kit — coming via registration
- Explore all topics: Topics
This page contains personal educational material by Frank Kienle. Views are his own. Examples are based on public data, educational material, or synthetic examples unless stated otherwise. No employer-confidential, customer-confidential, or supplier-confidential information is shared.