profcomff/rating-api
Aug 2025 – Aug 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
API DevelopmentBackend DevelopmentCode FormattingDatabase ManagementDatabase Migrations
Vladislav
PROFILE
Vladislav
Developed the Lecturer Ratings System for the profcomff/rating-api repository, introducing a dedicated database table and model to store lecturer ratings and implementing an API endpoint for creating new rating entries. Focused on backend development using Python and SQL, the work included careful database modeling and API design to ensure structured feedback collection and support future analytics. Code quality was enhanced through targeted linting and formatting improvements tied to the new feature, contributing to maintainability and stability. This update positioned the rating-api for quality assurance and release, enabling reliable data capture and supporting business needs for lecturer evaluation and analytics.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
121
Activity Months1
Your Network
21 peopleShared Repositories
21
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for profcomff/rating-api: Delivered the Lecturer Ratings System feature, including a new database table/model for storing lecturer ratings and an API endpoint to create rating entries. Commit activity focused on endpoint logic and code quality cleanup (post-rank-handler; lint-fix). No major bugs fixed this month; minor stability improvements came from lint cleaning tied to the feature. Overall impact: enables structured lecturer feedback, supports analytics, and improves data integrity and maintainability. Technologies demonstrated: REST API design, database modeling, linting/code quality tooling, and disciplined commit practices. This work positions the rating-api for QA and release readiness with clear business value in rating capture and analytics.
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for profcomff/rating-api: Delivered the Lecturer Ratings System feature, including a new database table/model for storing lecturer ratings and an API endpoint to create rating entries. Commit activity focused on endpoint logic and code quality cleanup (post-rank-handler; lint-fix). No major bugs fixed this month; minor stability improvements came from lint cleaning tied to the feature. Overall impact: enables structured lecturer feedback, supports analytics, and improves data integrity and maintainability. Technologies demonstrated: REST API design, database modeling, linting/code quality tooling, and disciplined commit practices. This work positions the rating-api for QA and release readiness with clear business value in rating capture and analytics.
August 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
API DevelopmentBackend DevelopmentCode FormattingDatabase ManagementDatabase Migrations
Repositories Contributed To
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