
Contributed to the Diplomado_PUCP repository by building and refining data analysis workflows and assignment scaffolding for Group 10 over a two-month period. Developed robust Jupyter Notebook environments and Python-based data ingestion scripts to streamline onboarding and enable reproducible analytics for the 2025 cohort. Enhanced documentation, managed notebook lifecycle, and improved repository clarity through targeted cleanup. Leveraged skills in Pandas, NumPy, and version control best practices to deliver ready-to-run examples, standardized data pipelines, and collaborative resources. Addressed minor bugs and maintained cross-notebook consistency, resulting in improved data availability, faster analytics cycles, and a more maintainable, production-ready analytics environment.
September 2025 delivered foundational data ingestion and notebook enhancements for Diplomado PUCP, enabling reliable data onboarding and faster analytics for the 2025 cohort. Key features delivered include a Data Ingestion Setup for base and DB2 data, and substantial EVA2 notebook development with updates to group_10_2025_eva2 and g10-v1. Additional notebook work covered updates to Tarea3.ipynb and the Group 10 Assignment 3 notebook (2025-09 batch), driving reproducible analysis workflows and readiness for production-like data pipelines. Minor issues were addressed where observed, with no major bugs reported. Overall impact includes improved data availability, faster analytics cycles, and stronger collaboration through standardized notebooks. Technologies demonstrated include Python-based data ingestion scripts, Jupyter notebooks, Git-based version control, and notebook-driven analytics (EVA2, Tarea3, Group 10).
September 2025 delivered foundational data ingestion and notebook enhancements for Diplomado PUCP, enabling reliable data onboarding and faster analytics for the 2025 cohort. Key features delivered include a Data Ingestion Setup for base and DB2 data, and substantial EVA2 notebook development with updates to group_10_2025_eva2 and g10-v1. Additional notebook work covered updates to Tarea3.ipynb and the Group 10 Assignment 3 notebook (2025-09 batch), driving reproducible analysis workflows and readiness for production-like data pipelines. Minor issues were addressed where observed, with no major bugs reported. Overall impact includes improved data availability, faster analytics cycles, and stronger collaboration through standardized notebooks. Technologies demonstrated include Python-based data ingestion scripts, Jupyter notebooks, Git-based version control, and notebook-driven analytics (EVA2, Tarea3, Group 10).
During August 2025, delivered Group 10 QLab assignment setup for Diplomado PUCP with full scaffolding, group qlab, movies resources, and initial notebooks/docs to support the 2025-qlab assignment. Expanded and refined Movies content and documentation, including group 10 movie docs and Dani's notes, plus added Dani's Assignment 2 scripts for parts 3–5. Implemented Grupo 10 Notebook enhancements and lifecycle management (tarea1-parte2 updates, new grupo_10 notebook, and lifecycle maintenance). Added QLab EVA2 notebook support and updates to enable EVA2 workflow. Performed repository cleanup by removing obsolete files to improve clarity and reduce confusion. Overall, the work enhances onboarding, reproducibility, collaboration, and delivery of the QLab-based assignment with ready-to-run examples and robust documentation.
During August 2025, delivered Group 10 QLab assignment setup for Diplomado PUCP with full scaffolding, group qlab, movies resources, and initial notebooks/docs to support the 2025-qlab assignment. Expanded and refined Movies content and documentation, including group 10 movie docs and Dani's notes, plus added Dani's Assignment 2 scripts for parts 3–5. Implemented Grupo 10 Notebook enhancements and lifecycle management (tarea1-parte2 updates, new grupo_10 notebook, and lifecycle maintenance). Added QLab EVA2 notebook support and updates to enable EVA2 workflow. Performed repository cleanup by removing obsolete files to improve clarity and reduce confusion. Overall, the work enhances onboarding, reproducibility, collaboration, and delivery of the QLab-based assignment with ready-to-run examples and robust documentation.

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