QLAB-Courses/summer_python_econ
Jan 2025 – Jan 2025
1 Month active
Languages Used
JSONJupyter NotebookPython
Technical Skills
Algorithmic ThinkingAlgorithmsConditional StatementsData AnalysisData CleaningData Manipulation
aguila-a
PROFILE
Aguila-a
Contributed to the QLAB-Courses/summer_python_econ repository by developing three new Jupyter notebooks focused on core Python data science concepts, including NumPy demonstrations, Pandas data analysis workflows, and object-oriented programming exercises. Applied skills in Python programming, data manipulation, and algorithmic thinking to create assignments that guide users through practical tasks such as array operations, data cleaning, and class design. Improved repository hygiene by correcting notebook execution metadata and removing outdated materials, ensuring reproducibility and clarity for students. Addressed two bugs related to execution tracking and content maintenance, resulting in a more reliable and streamlined learning environment for course participants.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
3
Lines of code
3,268
Activity Months1
Your Network
65 peopleWork History
January 2025
11 Commits • 3 Features
Jan 1, 2025January 2025 performance summary for QLAB-Courses/summer_python_econ. Delivered three new notebooks focusing on core Python data science skills (NumPy, Pandas, and OOP) and implemented repo hygiene improvements. Fixed notebook execution tracking to ensure reliable re-run states and removed superseded materials to prevent confusion. Result: improved student learning workflows, reproducibility, and content maintainability with clear traceability to commits.
11 Commits • 3 Features
Jan 1, 2025January 2025 performance summary for QLAB-Courses/summer_python_econ. Delivered three new notebooks focusing on core Python data science skills (NumPy, Pandas, and OOP) and implemented repo hygiene improvements. Fixed notebook execution tracking to ensure reliable re-run states and removed superseded materials to prevent confusion. Result: improved student learning workflows, reproducibility, and content maintainability with clear traceability to commits.
January 2025
Activity
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Quality Metrics
Correctness86.4%
Maintainability85.4%
Architecture83.6%
Performance81.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
Algorithmic ThinkingAlgorithmsConditional StatementsData AnalysisData CleaningData ManipulationData StructuresGitJupyter NotebookJupyter NotebooksLoopsMathematical FunctionsNumPyObject-Oriented ProgrammingPandas
Repositories Contributed To
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