SCE-Development/spartan-compass
Apr 2025 – Apr 2025
1 Month active
Languages Used
JavaScriptPythonSQLTypeScript
Technical Skills
Backend DevelopmentDatabase ManagementFrontend DevelopmentFull Stack DevelopmentTypeScript DevelopmentWeb Scraping
dvegas03_Desktop
PROFILE
Dvegas03_desktop
In April 2025, Khashayar Hardani enhanced the SCE-Development/spartan-compass repository by implementing an average rating feature for professors, integrating both backend and frontend components. He designed and migrated the database schema to store avg_rating, replaced placeholder values with actual data, and updated the UI to display ratings or indicate when none are available. By removing the legacy Python-based web scraper and centralizing the ratings flow, he improved data reliability and reduced maintenance complexity. His work involved TypeScript, SQL, and JavaScript, demonstrating full stack development skills while addressing data migration, UI clarity, and robust handling of missing or incomplete information.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
448
Activity Months1
Your Network
9 people
Work History
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Key features delivered: - Professor Average Rating: Implemented avg_rating in the database and integrated frontend display on course/professor pages. This includes migrating the DB to store average ratings and surfacing actual values instead of placeholders. UI now shows 'No rating' when missing or zero. - Scraper removed and ratings flow simplified: Removed the Python-based web scraper that previously populated ratings and replaced with a consistent data source for ratings. Major bugs fixed: - Handled missing professors gracefully and ensured safe migrations of rating data with DB schema changes. - Removed legacy scraper artifacts from the data pipeline to prevent stale or inconsistent data. Overall impact and accomplishments: - Improved data quality and reliability of professor ratings across the platform, leading to higher user trust and engagement. - Reduced maintenance overhead by eliminating the legacy scraper and centralizing rating data handling. - Clear UX improvement with explicit messaging for missing ratings, reducing user confusion on pages. Technologies/skills demonstrated: - Backend DB schema design and migrations (avg_rating), data migration considerations. - Frontend integration and UI/UX enhancements for rating display. - Legacy code cleanup and removal of Python-based scraper components. - Version control discipline and traceability with commit references. Business value: - More accurate, visible ratings directly on professor/course pages drives engagement and informed decisions for students, while reducing maintenance risk and data quality issues.
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 Key features delivered: - Professor Average Rating: Implemented avg_rating in the database and integrated frontend display on course/professor pages. This includes migrating the DB to store average ratings and surfacing actual values instead of placeholders. UI now shows 'No rating' when missing or zero. - Scraper removed and ratings flow simplified: Removed the Python-based web scraper that previously populated ratings and replaced with a consistent data source for ratings. Major bugs fixed: - Handled missing professors gracefully and ensured safe migrations of rating data with DB schema changes. - Removed legacy scraper artifacts from the data pipeline to prevent stale or inconsistent data. Overall impact and accomplishments: - Improved data quality and reliability of professor ratings across the platform, leading to higher user trust and engagement. - Reduced maintenance overhead by eliminating the legacy scraper and centralizing rating data handling. - Clear UX improvement with explicit messaging for missing ratings, reducing user confusion on pages. Technologies/skills demonstrated: - Backend DB schema design and migrations (avg_rating), data migration considerations. - Frontend integration and UI/UX enhancements for rating display. - Legacy code cleanup and removal of Python-based scraper components. - Version control discipline and traceability with commit references. Business value: - More accurate, visible ratings directly on professor/course pages drives engagement and informed decisions for students, while reducing maintenance risk and data quality issues.
April 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaScriptPythonSQLTypeScript
Technical Skills
Backend DevelopmentDatabase ManagementFrontend DevelopmentFull Stack DevelopmentTypeScript DevelopmentWeb Scraping
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