dataforgoodfr/13_reveler_inegalites_cinema
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingDatabase DesignDatabase Management
ndretex
PROFILE
Ndretex
Joel Teixeira developed the foundational data infrastructure for the dataforgoodfr/13_reveler_inegalites_cinema project, focusing on scalable data ingestion and analytics to support research on cinema access inequalities. He designed and initialized a relational database schema in SQL and SQLAlchemy, covering films, directors, producers, distributors, and festivals, and seeded it with international festival data. Joel enhanced data quality by implementing Python-based name matching algorithms, including Levenshtein and Jaro-Winkler, to improve cross-dataset matching. He also refactored data ingestion notebooks using Pandas, increasing reliability and maintainability. His work established a reproducible, auditable foundation for ongoing data-driven analysis in the repository.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
7,403
Activity Months1
Your Network
12 peopleShared Repositories
12
Audrey DuranMember
FitzfoufouMember
Florian_YunMember
hadrien PoinseauxMember
Julien-CommesMember
LudivineLacourMember
Nicolas RevelMember
Paul GabrielMember
lina kaciMember
Work History
February 2025
4 Commits • 3 Features
Feb 1, 2025In February 2025, delivered a solid data foundation for the dataforgoodfr/13_reveler_inegalites_cinema project, enabling scalable data ingestion, improved data quality, and reliable analytics for festival coverage and film data. Key outcomes include schema initialization, data enrichment, and robust ingestion notebooks, all designed to support cross-dataset matching and data-driven decision making for inequalities in cinema access.
4 Commits • 3 Features
Feb 1, 2025In February 2025, delivered a solid data foundation for the dataforgoodfr/13_reveler_inegalites_cinema project, enabling scalable data ingestion, improved data quality, and reliable analytics for festival coverage and film data. Key outcomes include schema initialization, data enrichment, and robust ingestion notebooks, all designed to support cross-dataset matching and data-driven decision making for inequalities in cinema access.
February 2025
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingDatabase DesignDatabase ManagementPandasPythonSQLSQLAlchemySchema InitializationString Matching Algorithms
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