NewsAppsUMD/maryland_voter_data
Mar 2025 – May 2025
2 Months active
Languages Used
CSVCSSHTMLJavaScriptPython
Technical Skills
Data EntryData ManagementChart.jsData VisualizationFlaskFront End Development
Kriti Singh
PROFILE
Kriti Singh
Kriti Sgh worked on the NewsAppsUMD/maryland_voter_data repository, focusing on scalable data ingestion and interactive data visualization for Maryland voter datasets. She established a robust CSV data scaffolding system using Python and data management best practices, enabling future ingestion workflows and supporting downstream analytics. In later work, Kriti developed dynamic voter turnout visualizations, including county-level dumbbell and bubble charts, leveraging JavaScript, Chart.js, and PapaParse to provide interactive, party-filtered analysis. Her approach emphasized maintainable front-end development and responsive UI, resulting in a flexible foundation for election analytics dashboards and improved data-driven insights for researchers and campaign stakeholders.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
4
Lines of code
579
Activity Months2
Your Network
49 people
Work History
May 2025
6 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NewsAppsUMD/maryland_voter_data. Key features delivered include interactive turnout visualizations with county-level dumbbell charts and a combined dumbbell/bubble chart for party-filtered turnout analysis. Bubble chart enhancements added dynamic party-based headlines, regression line, and inclusion of Green and Other parties, plus improved scaling and data points. These visuals enable clearer storytelling and data-driven decision support for campaigns and researchers. Technologies demonstrated: front-end data visualization, data filtering, dynamic UI, and responsive charts. Overall impact: faster iteration on election analytics, richer insights into partisan turnout patterns, and a scalable pattern for future dashboards.
6 Commits • 2 Features
May 1, 2025May 2025 monthly summary for NewsAppsUMD/maryland_voter_data. Key features delivered include interactive turnout visualizations with county-level dumbbell charts and a combined dumbbell/bubble chart for party-filtered turnout analysis. Bubble chart enhancements added dynamic party-based headlines, regression line, and inclusion of Green and Other parties, plus improved scaling and data points. These visuals enable clearer storytelling and data-driven decision support for campaigns and researchers. Technologies demonstrated: front-end data visualization, data filtering, dynamic UI, and responsive charts. Overall impact: faster iteration on election analytics, richer insights into partisan turnout patterns, and a scalable pattern for future dashboards.
May 2025
March 2025
8 Commits • 2 Features
Mar 1, 2025March 2025: Focused on establishing scalable data ingestion readiness for the Maryland voter dataset. Implemented foundational CSV scaffolding across dataset areas, enabling future ingestion workflows, and added the authoritative 2020 inactive voters data by county and political party to support analysis. Performed cleanup and governance hygiene by removing unused placeholders to prevent drift. Prepared the repository for ongoing data engineering tasks and reporting pipelines.
March 2025
8 Commits • 2 Features
Mar 1, 2025March 2025: Focused on establishing scalable data ingestion readiness for the Maryland voter dataset. Implemented foundational CSV scaffolding across dataset areas, enabling future ingestion workflows, and added the authoritative 2020 inactive voters data by county and political party to support analysis. Performed cleanup and governance hygiene by removing unused placeholders to prevent drift. Prepared the repository for ongoing data engineering tasks and reporting pipelines.
Activity
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Quality Metrics
Correctness89.4%
Maintainability87.2%
Architecture87.2%
Performance87.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSCSVHTMLJavaScriptPython
Technical Skills
Chart.jsData EntryData ManagementData VisualizationFlaskFront End DevelopmentFrontend DevelopmentJavaScriptPapaParsePython
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