dataforgoodfr/13_eclaireur_public
Aug 2025 – Sep 2025
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentData AnalysisData EngineeringData CleaningRegular Expressions
Marjorie-J
PROFILE
Marjorie-j
Marjorie Janda developed data enrichment and cleaning features for the dataforgoodfr/13_eclaireur_public repository, focusing on backend development and data engineering using Python and regular expressions. She delivered a module that calculates and integrates average subsidy and public procurement metrics by community type, enabling robust benchmarking and policy analysis across regional, departmental, and national levels. In a subsequent release, she refactored the data layer to standardize naming conventions for collectivités, applying regex-based normalization to improve data uniformity and reliability. Her work demonstrated depth in data analysis and cleaning, resulting in more consistent, analytics-ready datasets for downstream reporting and comparison.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
222
Activity Months2
Your Network
15 peopleShared Repositories
15
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Key feature delivered and major fix focused on data hygiene and naming consistency in the data layer for analytics and reporting.
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Key feature delivered and major fix focused on data hygiene and naming consistency in the data layer for analytics and reporting.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08 highlighting business value and technical achievements: Delivered the average subsidy and public procurement enrichment feature for communities with regional, departmental, and national averages by community type, enabling robust benchmarking and policy analysis.
August 2025
1 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08 highlighting business value and technical achievements: Delivered the average subsidy and public procurement enrichment feature for communities with regional, departmental, and national averages by community type, enabling robust benchmarking and policy analysis.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentData AnalysisData CleaningData EngineeringRegular Expressions
Repositories Contributed To
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