QLAB-Courses/summer_python_econ
Jan 2025 – Jan 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Algorithm ImplementationBasic Input/OutputConditional LogicData AnalysisJupyter NotebookJupyter Notebooks
Pablo Surco
PROFILE
Pablo Surco
During January 2025, a20212201@pucp.edu.pe contributed to the QLAB-Courses/summer_python_econ repository by developing a user-facing voting eligibility checker and enhancing Jupyter Notebook learning materials. Their work involved implementing Python-based input/output and conditional logic to determine voting eligibility based on user age, providing a practical example for learners. They also expanded the educational content with Numpy-powered array operations and introduced time conversion utilities, reinforcing hands-on data analysis and code reuse. The focus remained on feature delivery and content enrichment, with no major bugs reported, demonstrating a foundational approach to both technical problem-solving and educational resource development within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
267
Activity Months1
Your Network
65 people
Work History
January 2025
3 Commits • 2 Features
Jan 1, 2025January 2025: Focused feature delivery and learning-content improvements for QLAB-Courses/summer_python_econ, delivering tangible business value and demonstrable technical achievements. The work centers on a user-facing voting eligibility checker and enhanced notebook materials that include practical array operations demonstrations and time utilities, reinforcing hands-on learning and code reuse.
3 Commits • 2 Features
Jan 1, 2025January 2025: Focused feature delivery and learning-content improvements for QLAB-Courses/summer_python_econ, delivering tangible business value and demonstrable technical achievements. The work centers on a user-facing voting eligibility checker and enhanced notebook materials that include practical array operations demonstrations and time utilities, reinforcing hands-on learning and code reuse.
January 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability86.6%
Architecture80.0%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Algorithm ImplementationBasic Input/OutputConditional LogicData AnalysisJupyter NotebookJupyter NotebooksNumpyPython Programming
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