alexanderquispe/Diplomado_PUCP
Aug 2025 – Sep 2025
2 Months active
Languages Used
Jupyter NotebookNumPyPythonSQL
Technical Skills
Algorithmic ThinkingData AnalysisData CleaningData LoadingData StructuresNumPy
jazmincatalan
PROFILE
Jazmincatalan
Jazmin Catalan developed educational data analysis features for the alexanderquispe/Diplomado_PUCP repository, focusing on reproducible workflows and hands-on learning resources. She built Jupyter Notebooks that guide users through NumPy-based matrix analytics, including random matrix generation, column means, and diagonal sums, as well as a movie summary function that transforms dictionaries into descriptive sentences. Jazmin also established a data cleaning and loading pipeline for the ENAHO Education dataset, emphasizing early exploration and null-value handling. Her work leveraged Python, NumPy, and Pandas, providing clear, well-documented code that supports both onboarding and practical skill development in data analysis and control flow.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
1,500
Activity Months2
Your Network
89 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for alexanderquispe/Diplomado_PUCP: Delivered two educational Jupyter notebooks (NumPy-based Movie Summary and Python Control Flow Tutorial) to strengthen learner onboarding and practical data analysis skills. The NumPy notebook demonstrates randomized matrix generation, column means, and diagonal sums; the control flow notebook provides interactive examples of pass, continue, and break. Commit dd8283821f60670f175c0a9ad27fdb78de679777 (#1700 parte1_creada). No major bugs reported this month. This work enhances reproducibility, self-paced learning, and Python/NumPy proficiency across the Diplomado_PUCP repository.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for alexanderquispe/Diplomado_PUCP: Delivered two educational Jupyter notebooks (NumPy-based Movie Summary and Python Control Flow Tutorial) to strengthen learner onboarding and practical data analysis skills. The NumPy notebook demonstrates randomized matrix generation, column means, and diagonal sums; the control flow notebook provides interactive examples of pass, continue, and break. Commit dd8283821f60670f175c0a9ad27fdb78de679777 (#1700 parte1_creada). No major bugs reported this month. This work enhances reproducibility, self-paced learning, and Python/NumPy proficiency across the Diplomado_PUCP repository.
September 2025
August 2025
3 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for alexanderquispe/Diplomado_PUCP. Focused on delivering two high-impact features and establishing data analysis pipelines that create business value and technical momentum. Key features delivered: (1) Movie summary function and NumPy-based matrix analytics implemented as a Jupyter Notebook feature; the notebook defines movie_summary to generate descriptive sentences from a dictionary of movies and includes 4x4 random matrix analysis (column means and diagonal sum). Commits: 08ce864f1fae1251d1523da4a8dc20a046fa053b; 388b0adda53db239d92ff564bf2cf20b39954607. (2) ENAHO Education data analysis setup: initial module analysis including libraries import, dataset loading, early exploration, relevant column selection, and null-value cleaning. Commit: 6ac04d108c87945cc4ec1347f763f60e5048731a. These efforts are complemented by clean, well-documented commits and a focus on reproducibility.
August 2025
3 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for alexanderquispe/Diplomado_PUCP. Focused on delivering two high-impact features and establishing data analysis pipelines that create business value and technical momentum. Key features delivered: (1) Movie summary function and NumPy-based matrix analytics implemented as a Jupyter Notebook feature; the notebook defines movie_summary to generate descriptive sentences from a dictionary of movies and includes 4x4 random matrix analysis (column means and diagonal sum). Commits: 08ce864f1fae1251d1523da4a8dc20a046fa053b; 388b0adda53db239d92ff564bf2cf20b39954607. (2) ENAHO Education data analysis setup: initial module analysis including libraries import, dataset loading, early exploration, relevant column selection, and null-value cleaning. Commit: 6ac04d108c87945cc4ec1347f763f60e5048731a. These efforts are complemented by clean, well-documented commits and a focus on reproducibility.
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookNumPyPythonSQL
Technical Skills
Algorithmic ThinkingControl Flow StatementsData AnalysisData CleaningData LoadingData StructuresNumPyPandasPython Programming
Repositories Contributed To
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