ScottyLabs/cmucourses
Jan 2025 – Feb 2025
2 Months active
Languages Used
TypeScriptJavaScript
Technical Skills
Frontend DevelopmentReactState Management
Amanda Lu
PROFILE
Amanda Lu
Amanda Lu contributed to the ScottyLabs/cmucourses repository by enhancing the accuracy and usability of course evaluation analytics. She implemented respondent-weighted normalization in JavaScript and TypeScript, refining the aggregation of FCE data to produce more reliable evaluation scores for stakeholders. Amanda also addressed a critical bug in the department filter, ensuring that search queries reset appropriately when users switch departments, which improved the frontend user experience. Her work demonstrated a strong grasp of React and state management, with careful attention to code quality and traceability. Over two months, Amanda delivered targeted, well-scoped solutions that improved both data reliability and interface usability.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
17
Activity Months2
Your Network
8 people
Work History
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary for ScottyLabs/cmucourses focused on improving UI usability and ensuring stable filtering behavior. Delivered a targeted fix to the Department Filter that removes stale search input upon department changes, reducing user friction during course discovery and maintaining clean, predictable filter state across navigations.
1 Commits
Feb 1, 2025February 2025 monthly summary for ScottyLabs/cmucourses focused on improving UI usability and ensuring stable filtering behavior. Delivered a targeted fix to the Department Filter that removes stale search input upon department changes, reducing user friction during course discovery and maintaining clean, predictable filter state across navigations.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 - ScottyLabs/cmucourses: Delivered a more accurate FCE data aggregation by applying respondent-weighted normalization, significantly improving the reliability of course evaluation scores. Implemented a weighting fix (#190) which corrected the data weighting logic. Result: clearer analytics for course improvements and better decision-making for stakeholders. Demonstrated data engineering, feature engineering, and code quality practices across the repo.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 - ScottyLabs/cmucourses: Delivered a more accurate FCE data aggregation by applying respondent-weighted normalization, significantly improving the reliability of course evaluation scores. Implemented a weighting fix (#190) which corrected the data weighting logic. Result: clearer analytics for course improvements and better decision-making for stakeholders. Demonstrated data engineering, feature engineering, and code quality practices across the repo.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture80.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaScriptTypeScript
Technical Skills
Frontend DevelopmentReactState Management
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