
Over a three-month period, contributed to the alexanderquispe/Diplomado_PUCP repository by developing data analysis and geospatial workflows focused on South American datasets. Built automated Jupyter notebooks for CSV preprocessing, analytics, and reporting, including exporting results to Word documents using Python and Pandas. Delivered interactive geographic visualizations and district-level COVID-19 heatmaps with Folium and GeoPandas, and automated web scraping of historical election data with Selenium. Developed a reproducible raster data analysis tutorial for district-level precipitation in Peru, leveraging Rasterio and Matplotlib for visualization and zonal statistics preparation. The work emphasized reproducibility, robust data pipelines, and clear documentation throughout each feature.
Month: 2025-10 — Delivered a new Peru District-Level Precipitation Tutorial: an end-to-end raster workflow to analyze monthly precipitation by Peru's districts, including visualization, clipping rasters to district boundaries, computing district-averaged precipitation, and map-based results with basic statistical context. Notebook is prepared for zonal statistics (zonal_stats) and ready for re-execution to support repeatable analyses. Commits to production: 6414bf0c690200b99b73e7933d39a81c0335724b (update raster tutorial); a9fdf21a38acb8f0c779392a61e0186f69756a60 (update code). No major bugs reported this month; minor refinements were applied as part of the feature updates.
Month: 2025-10 — Delivered a new Peru District-Level Precipitation Tutorial: an end-to-end raster workflow to analyze monthly precipitation by Peru's districts, including visualization, clipping rasters to district boundaries, computing district-averaged precipitation, and map-based results with basic statistical context. Notebook is prepared for zonal statistics (zonal_stats) and ready for re-execution to support repeatable analyses. Commits to production: 6414bf0c690200b99b73e7933d39a81c0335724b (update raster tutorial); a9fdf21a38acb8f0c779392a61e0186f69756a60 (update code). No major bugs reported this month; minor refinements were applied as part of the feature updates.
September 2025 monthly summary for alexanderquispe/Diplomado_PUCP: Delivered three primary features with measurable business value across health analytics, election data automation, and geographic visualization. No explicit major bug fixes documented this month; the focus was on feature delivery and code quality improvements to support robust, repeatable analyses. Technologies demonstrated include Python, Folium, Geopandas, Chromedriver-based web scraping, and Jupyter notebooks, reinforcing data science workflow maturity and reproducibility.
September 2025 monthly summary for alexanderquispe/Diplomado_PUCP: Delivered three primary features with measurable business value across health analytics, election data automation, and geographic visualization. No explicit major bug fixes documented this month; the focus was on feature delivery and code quality improvements to support robust, repeatable analyses. Technologies demonstrated include Python, Folium, Geopandas, Chromedriver-based web scraping, and Jupyter notebooks, reinforcing data science workflow maturity and reproducibility.
Delivered an automated data analysis notebook setup with preprocessing and reporting for the Diplomado_PUCP project. Implemented data loading from CSV, column renaming, filtering to South American countries, sorting, and basic analytics (averages, handling missing values). Added end-to-end export of analysis results to a Word document (.docx) from a pandas DataFrame to support reporting.
Delivered an automated data analysis notebook setup with preprocessing and reporting for the Diplomado_PUCP project. Implemented data loading from CSV, column renaming, filtering to South American countries, sorting, and basic analytics (averages, handling missing values). Added end-to-end export of analysis results to a Word document (.docx) from a pandas DataFrame to support reporting.

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