alexanderquispe/Diplomado_PUCP
Aug 2025 – Oct 2025
3 Months active
Languages Used
JSONJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData StructuresData TypesDictionariesFile Management
anelvc96
PROFILE
Anelvc96
Anel Vasquez contributed to the alexanderquispe/Diplomado_PUCP repository by developing data preparation pipelines, educational resources, and codebase improvements over a three-month period. She built unified analytics workflows for Enaho01 datasets, implementing data loading, cleaning, merging, and statistical aggregation using Python and pandas to streamline analysis and support data-driven decisions. Anel also expanded Python programming coverage with Jupyter notebooks focused on functions, classes, and data analysis, enhancing hands-on learning for students. Her work emphasized maintainability by removing outdated files and modularizing content, demonstrating depth in data manipulation, object-oriented programming, and reproducible research practices within collaborative software development environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
5
Lines of code
15,034
Activity Months3
Your Network
41 peopleSame Organization
@pucp.pe1
Work History
October 2025
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly performance summary for alexanderquispe/Diplomado_PUCP. Focused on delivering practical learning resources and laying groundwork for upcoming modules. Key outputs include two educational notebooks for Python programming concepts and a new content skeleton for Clase20septiembre, supported by clear commit references. These contributions enhance student hands-on practice, accelerate future content development, and demonstrate strong proficiency in Python, data analysis with pandas, and Jupyter-based workflows.
2 Commits • 2 Features
Oct 1, 2025October 2025 monthly performance summary for alexanderquispe/Diplomado_PUCP. Focused on delivering practical learning resources and laying groundwork for upcoming modules. Key outputs include two educational notebooks for Python programming concepts and a new content skeleton for Clase20septiembre, supported by clear commit references. These contributions enhance student hands-on practice, accelerate future content development, and demonstrate strong proficiency in Python, data analysis with pandas, and Jupyter-based workflows.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for alexanderquispe/Diplomado_PUCP, focusing on delivering a unified data preparation and analytics pipeline for Enaho01 datasets. Highlights include loading multiple datasets, clean data preparation, merge and aggregation steps, and the computation of statistical indicators to enable data-driven decision making. Work demonstrates strong ETL, data wrangling, and analytics capabilities with clear traceability.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for alexanderquispe/Diplomado_PUCP, focusing on delivering a unified data preparation and analytics pipeline for Enaho01 datasets. Highlights include loading multiple datasets, clean data preparation, merge and aggregation steps, and the computation of statistical indicators to enable data-driven decision making. Work demonstrates strong ETL, data wrangling, and analytics capabilities with clear traceability.
August 2025
4 Commits • 2 Features
Aug 1, 2025August 2025 (2025-08) — Delivered focused enhancements to alexanderquispe/Diplomado_PUCP, with emphasis on data-processing capabilities and repository quality. Key technical achievements include expanding Python data handling and NumPy operations in Assignment 1, and cleaning the codebase by removing outdated notebooks. These efforts improve learning outcomes, reproducibility, and maintainability, aligning with course objectives and workflow standards in the team.
4 Commits • 2 Features
Aug 1, 2025August 2025 (2025-08) — Delivered focused enhancements to alexanderquispe/Diplomado_PUCP, with emphasis on data-processing capabilities and repository quality. Key technical achievements include expanding Python data handling and NumPy operations in Assignment 1, and cleaning the codebase by removing outdated notebooks. These efforts improve learning outcomes, reproducibility, and maintainability, aligning with course objectives and workflow standards in the team.
August 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance88.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
ClassesData AnalysisData CleaningData ManipulationData MergingData StructuresData TypesDictionariesFile ManagementFunctionsListsNumPyObject-Oriented ProgrammingPandasPython Programming
Repositories Contributed To
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