BU-Spark/ds-bcc-liz-breadon-accountability
Oct 2024 – Dec 2024
2 Months active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData CleaningData MergingData VisualizationJupyter NotebookLooker Studio
Christine Sangphet
PROFILE
Christine Sangphet
Sangphet developed two end-to-end data analytics features for the BU-Spark/ds-bcc-liz-breadon-accountability repository, focusing on building violations and housing compliance. Over two months, Sangphet built reproducible Jupyter Notebooks in Python and SQL to clean, merge, and enrich violation datasets, exporting refined data to CSV for downstream analysis. The work included integrating with Looker Studio, preparing address-level analytics, and mapping violation descriptions to severity levels for risk assessment. By emphasizing data quality, workflow traceability, and visualization readiness, Sangphet enabled faster dashboard setup and actionable insights for landlord communications, demonstrating depth in data processing, visualization, and scalable analytics workflow design.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
342,821
Activity Months2
Your Network
51 peopleWork History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for BU-Spark/ds-bcc-liz-breadon-accountability: Delivered a new Building Violations Analysis Notebook that maps violation descriptions to severity levels, exports processed data to CSV, and prepares data for visualization and landlord analytics in Looker Studio. The work enhances risk visibility, enables proactive compliance insights, and lays the foundation for data-driven landlord communications.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for BU-Spark/ds-bcc-liz-breadon-accountability: Delivered a new Building Violations Analysis Notebook that maps violation descriptions to severity levels, exports processed data to CSV, and prepares data for visualization and landlord analytics in Looker Studio. The work enhances risk visibility, enables proactive compliance insights, and lays the foundation for data-driven landlord communications.
December 2024
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024: Delivered a data cleaning and Looker Studio integration feature in BU-Spark/ds-bcc-liz-breadon-accountability. Key outcomes include a cleaned 2016+ violations dataset with refined addresses and contact info, exported as CSV, plus a Looker Studio-ready workflow to merge violations with student housing addresses for dashboard analysis (pie chart of top violations). Major bugs fixed: None reported this month. Overall impact and accomplishments: Improved data quality and accessibility of violation analytics, enabling faster dashboard readiness and data-driven decision-making for housing and compliance initiatives. The reproducible notebooks and CSV exports lay groundwork for scalable analytics and ongoing monitoring of top violation types. Technologies/skills demonstrated: Python data wrangling, Jupyter notebooks, CSV I/O, data merging/enrichment, Looker Studio integration, data visualization preparation, and emphasis on reproducible workflows with commit traceability.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024: Delivered a data cleaning and Looker Studio integration feature in BU-Spark/ds-bcc-liz-breadon-accountability. Key outcomes include a cleaned 2016+ violations dataset with refined addresses and contact info, exported as CSV, plus a Looker Studio-ready workflow to merge violations with student housing addresses for dashboard analysis (pie chart of top violations). Major bugs fixed: None reported this month. Overall impact and accomplishments: Improved data quality and accessibility of violation analytics, enabling faster dashboard readiness and data-driven decision-making for housing and compliance initiatives. The reproducible notebooks and CSV exports lay groundwork for scalable analytics and ongoing monitoring of top violation types. Technologies/skills demonstrated: Python data wrangling, Jupyter notebooks, CSV I/O, data merging/enrichment, Looker Studio integration, data visualization preparation, and emphasis on reproducible workflows with commit traceability.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData CleaningData MergingData ProcessingData VisualizationJupyter NotebookJupyter NotebooksLooker StudioMatplotlibPandas
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