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Data Analysis & Automation
Turning messy, multi-source datasets into decision-ready insights with Python, SQL, and automated reporting pipelines.
In Practice
Where automation actually pays off
Good analysis is judged by the decisions it enables, not the code behind it.
I automate the repetitive parts of analysis — data cleaning, spatial overlays, report generation — so more time goes into interpretation and communication. My toolkit spans Python (pandas, NumPy, xarray, geopandas), SQL, and Streamlit for building lightweight internal tools that make repeatable workflows self-serve.
- Automated multi-layer overlay analyses in ArcGIS/QGIS, cutting data-processing time by 50%.
- Automated Google Earth Engine workflows to assess turbidity and land-water change using remote-sensing indices.
- Built a personal Streamlit dashboard automating hazard exposure metrics for distributed asset portfolios.
- Developed automated web scraping workflows using Python and Selenium to extract and structure data from online sources.
Methods & Tools
What I build with
Selected Evidence
Related case study
Other Areas