
Contributed to the alexanderquispe/Diplomado_PUCP repository by developing a Jupyter Notebook that demonstrates core data handling concepts using Python, NumPy, and structured data types such as dictionaries and tuples. The work included implementing a function to summarize movie data and showcasing numerical operations with matrices, providing practical examples for learners. Addressed data accuracy by correcting typographical errors throughout the notebook, ensuring professional and consistent content. Expanded project documentation by adding a new section to guide future entries on favorite movies. These contributions improved code clarity, enhanced onboarding resources, and supported maintainability through clear demonstrations and comprehensive documentation updates.
For 2025-08, delivered tangible learning-content improvements in alexanderquispe/Diplomado_PUCP, including a Jupyter Notebook showcasing core data handling with dictionaries, tuples, a summarization function for movie data, and NumPy matrix usage; corrected typos in Jakeline across notebook content to uphold accuracy and professionalism; expanded documentation by adding a Favorite Movies 2 section with placeholders to guide future entries. This work increases code clarity, provides practical demonstrations for data structures and numerical operations, and improves documentation completeness, contributing to faster onboarding, higher-quality examples, and better maintainability.
For 2025-08, delivered tangible learning-content improvements in alexanderquispe/Diplomado_PUCP, including a Jupyter Notebook showcasing core data handling with dictionaries, tuples, a summarization function for movie data, and NumPy matrix usage; corrected typos in Jakeline across notebook content to uphold accuracy and professionalism; expanded documentation by adding a Favorite Movies 2 section with placeholders to guide future entries. This work increases code clarity, provides practical demonstrations for data structures and numerical operations, and improves documentation completeness, contributing to faster onboarding, higher-quality examples, and better maintainability.

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