ITACADEMYprojectes/ProjecteData
May 2025 – Jun 2025
2 Months active
Languages Used
GitJupyter NotebookPythonSQLShellCSV
Technical Skills
ANOVAChi-squared testClusteringData AnalysisData CleaningData Transformation
Roser Blasco
PROFILE
Roser Blasco
Roser Blasco developed advanced analytics and client reporting features for the ITACADEMYprojectes/ProjecteData repository over two months, focusing on scalable data-driven insights for financial clients. She engineered KPI frameworks, client segmentation, and risk modeling using Python, Pandas, and Jupyter Notebooks, integrating statistical methods such as ANOVA and clustering to enhance business analysis. Her work included robust data cleaning, visualization improvements with Matplotlib and Plotly, and the integration of external datasets for richer client profiling. By addressing data quality, UI clarity, and cross-platform compatibility, Roser delivered maintainable, extensible analytics modules that support accurate targeting and improved stakeholder communication across client segments.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
72Total
Bugs
8
Commits
72
Features
40
Lines of code
38,365
Activity Months2
Your Network
36 people
Work History
June 2025
25 Commits • 19 Features
Jun 1, 2025June 2025 performance summary for ITACADEMYprojectes/ProjecteData: Delivered extensive client analytics enhancements and visualization capabilities, enabling deeper client insights and data-driven decision-making. Implemented age-based segmentation, integrated INE data, and expanded charting and reporting across client segments and behavior. Introduced end-to-end data visualization improvements (pyramids for 2025, 2030, 2035; combined pyramids; age-distribution views for bank and INE data) and updated chart types to lines for trend clarity. Extended client reporting and extrapolation capabilities, and expanded support for mortgage and other financial products with filtering. Improved data quality by removing duplicates and fixing UI terminology. These efforts position the product for more accurate targeting, better stakeholder communication, and scalable analytics delivery.
25 Commits • 19 Features
Jun 1, 2025June 2025 performance summary for ITACADEMYprojectes/ProjecteData: Delivered extensive client analytics enhancements and visualization capabilities, enabling deeper client insights and data-driven decision-making. Implemented age-based segmentation, integrated INE data, and expanded charting and reporting across client segments and behavior. Introduced end-to-end data visualization improvements (pyramids for 2025, 2030, 2035; combined pyramids; age-distribution views for bank and INE data) and updated chart types to lines for trend clarity. Extended client reporting and extrapolation capabilities, and expanded support for mortgage and other financial products with filtering. Improved data quality by removing duplicates and fixing UI terminology. These efforts position the product for more accurate targeting, better stakeholder communication, and scalable analytics delivery.
June 2025
May 2025
47 Commits • 21 Features
May 1, 2025Monthly summary for ITACADEMYprojectes/ProjecteData – May 2025. Focused on establishing a robust analytics and client-delivery foundation, with emphasis on cross-platform development readiness, KPI-driven metrics, and enriched client analytics visuals and reporting. Key outcomes include Roser environment setup on VM and macOS, KPI framework initialization with KPI1–KPI5 calculations, and substantial client analytics enhancements (structure, Roser analysis, and improved visuals). Also delivered results module enhancements, data modeling/risk calculations, and a broad set of UI/visual improvements, alongside repo hygiene improvements to reduce clutter and improve maintainability.
May 2025
47 Commits • 21 Features
May 1, 2025Monthly summary for ITACADEMYprojectes/ProjecteData – May 2025. Focused on establishing a robust analytics and client-delivery foundation, with emphasis on cross-platform development readiness, KPI-driven metrics, and enriched client analytics visuals and reporting. Key outcomes include Roser environment setup on VM and macOS, KPI framework initialization with KPI1–KPI5 calculations, and substantial client analytics enhancements (structure, Roser analysis, and improved visuals). Also delivered results module enhancements, data modeling/risk calculations, and a broad set of UI/visual improvements, alongside repo hygiene improvements to reduce clutter and improve maintainability.
Activity
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Quality Metrics
Correctness86.2%
Maintainability85.6%
Architecture82.8%
Performance81.4%
AI Usage25.6%
Skills & Technologies
Programming Languages
CSVGitJupyter NotebookPythonSQLShell
Technical Skills
ANOVAChi-squared testClusteringData AnalysisData CleaningData EngineeringData LoadingData PreprocessingData ProcessingData TransformationData VisualizationExploratory Data Analysis (EDA)File ManagementJupyter NotebookJupyter Notebooks
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