ScottyLabs/cmucourses
Jan 2025 – Feb 2025
2 Months active
Languages Used
TypeScriptJavaScript
Technical Skills
Frontend DevelopmentReactState Management
Amanda Lu
PROFILE
Amanda Lu
Worked on the ScottyLabs/cmucourses repository to enhance the accuracy and usability of course evaluation analytics. Applied respondent-weighted normalization to the FCE data aggregation process, ensuring that evaluation scores more accurately reflected the number of respondents and improving the reliability of analytics for stakeholders. Addressed a critical data skew by correcting the weighting logic, demonstrating attention to data engineering and code quality. Additionally, improved frontend usability by fixing a Department Filter bug, ensuring that search queries reset appropriately when departments change. Utilized JavaScript, TypeScript, and React, with a focus on frontend development and state management to deliver these improvements.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
17
Activity Months2
Your Network
9 peopleShared Repositories
9
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
Repositories Contributed To
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