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
During January 2025, [Name] developed three new Jupyter notebooks for the QLAB-Courses/summer_python_econ repository, focusing on core Python data science skills such as NumPy, Pandas, and object-oriented programming. They implemented features including NumPy demonstrations, a Pandas data analysis guide, and exercises on Python functions and classes. Their work involved careful data manipulation, algorithmic thinking, and the use of Python and Jupyter Notebook to enhance student learning workflows. Additionally, [Name] addressed repository hygiene by correcting notebook execution metadata and removing outdated materials, resulting in improved reproducibility and maintainability. The contributions demonstrated solid technical depth and attention to workflow quality.
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 people
Work 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 Fixed notebook execution tracking to ensure reliable re-run states and removed superseded materials to prevent confusion.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 Fixed notebook execution tracking to ensure reliable re-run states and removed superseded materials to prevent confusion.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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