An interactive dashboard analysing Berlin's rental market by district, price, and trend — my capstone for Le Wagon's Data Analytics bootcamp.
- Extract — real-world Berlin rental listing data loaded into BigQuery
- Transform — cleaning and modelling with advanced SQL, organised into layered BigQuery datasets following the raw → intermediate → marts pattern, so each transformation step is isolated and reusable
- Visualise — interactive exploration in Data Studio: filter by district, price band, and time period
- BigQuery — data warehouse with layered datasets (raw / intermediate / marts)
- SQL — extraction, cleaning, aggregation
- Data Studio (Looker Studio) — interactive dashboard
Transit & Centrality Centrality drives rent more than transit proximity. |
Premium Living The high/low gap has grown 51% since 2020. |
Best Value Biesdorf, Hellersdorf and Marzahn lead on value score. |
- Trend forecasting per district
- Joining in open data on transport access and amenities to explain price variation