ITACADEMYprojectes/ProjecteData
May 2025 – Jun 2025
2 Months active
Languages Used
CSVHTMLJSONJupyter NotebookPower BIPythonSQLShell
Technical Skills
Business IntelligenceCode CleanupData AnalysisData CleaningData EngineeringData Imputation
Isanagui Rojas
PROFILE
Isanagui Rojas
Isanagui contributed to ITACADEMYprojectes/ProjecteData by developing robust data pipelines and enhancing analytics capabilities over a two-month period. They implemented data cleaning, transformation, and exploratory analysis using Python and Pandas, enabling reliable ETL workflows and actionable Power BI reporting for stakeholders. Their work included consolidating CSV ingestion, refining file path management, and expanding user-facing analytics with KPI separation and statistical testing. In June, Isanagui improved the project’s map interface through WebGL shader-based rendering, optimizing client-side performance and visual fidelity. Comprehensive documentation and artifact updates ensured traceability and clarity, reflecting a thorough and well-structured engineering approach throughout the repository.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
55Total
Bugs
4
Commits
55
Features
18
Lines of code
286,685
Activity Months2
Your Network
36 people
Work History
June 2025
16 Commits • 2 Features
Jun 1, 2025June 2025 focused on delivering high-value features and strengthening project artifacts for ITACADEMYprojectes/ProjecteData. Key outcomes include WebGL Shader-based Map Rendering Enhancements that improve visual fidelity and performance in the map UI, and a comprehensive Sprint 4 Documentation, Artifacts, and Presentation Deliverables Update with updated docs, file reorganizations, and refined Power BI pbix/docx/pdf assets. No major bugs recorded in the provided data; effort prioritized feature delivery and documentation. Overall impact includes a smoother, more responsive map experience, clearer stakeholder communication, and improved traceability for future sprints. Technologies demonstrated span WebGL shader development, client-side rendering optimization, and Power BI/presentation tooling, alongside robust documentation processes.
16 Commits • 2 Features
Jun 1, 2025June 2025 focused on delivering high-value features and strengthening project artifacts for ITACADEMYprojectes/ProjecteData. Key outcomes include WebGL Shader-based Map Rendering Enhancements that improve visual fidelity and performance in the map UI, and a comprehensive Sprint 4 Documentation, Artifacts, and Presentation Deliverables Update with updated docs, file reorganizations, and refined Power BI pbix/docx/pdf assets. No major bugs recorded in the provided data; effort prioritized feature delivery and documentation. Overall impact includes a smoother, more responsive map experience, clearer stakeholder communication, and improved traceability for future sprints. Technologies demonstrated span WebGL shader development, client-side rendering optimization, and Power BI/presentation tooling, alongside robust documentation processes.
June 2025
May 2025
39 Commits • 16 Features
May 1, 2025May 2025 performance summary for ITACADEMYprojectes/ProjecteData: Delivered a data-focused feature set, strengthened data quality, broadened analytics capabilities, and improved stability and reporting. Key outcomes include: robust test scaffolding with cleanup; Dataset Cleaning and Data Transformation to enable reliable analytics pipelines; expanded Exploratory Data Analysis (EDA) and Power BI reporting for users and customers with KPI separation and ANOVA tests; consolidated CSV data ingestion with removal of obsolete sources and path adjustments; targeted UX and documentation updates plus bug fixes for PPT/HTML artifacts and path handling. Result: reliable data pipelines, actionable insights for clients, improved stakeholder reporting, and faster sprint readiness.
May 2025
39 Commits • 16 Features
May 1, 2025May 2025 performance summary for ITACADEMYprojectes/ProjecteData: Delivered a data-focused feature set, strengthened data quality, broadened analytics capabilities, and improved stability and reporting. Key outcomes include: robust test scaffolding with cleanup; Dataset Cleaning and Data Transformation to enable reliable analytics pipelines; expanded Exploratory Data Analysis (EDA) and Power BI reporting for users and customers with KPI separation and ANOVA tests; consolidated CSV data ingestion with removal of obsolete sources and path adjustments; targeted UX and documentation updates plus bug fixes for PPT/HTML artifacts and path handling. Result: reliable data pipelines, actionable insights for clients, improved stakeholder reporting, and faster sprint readiness.
Activity
Loading activity data...
Quality Metrics
Correctness79.0%
Maintainability78.8%
Architecture77.4%
Performance77.4%
AI Usage21.8%
Skills & Technologies
Programming Languages
CSVGLSLHTMLJSONJupyter NotebookPower BIPythonSQLShell
Technical Skills
Business IntelligenceCode CleanupData AnalysisData CleaningData EngineeringData ImputationData ManagementData PreparationData TransformationData VisualizationData WranglingDatabase ConnectionEDAETLExploratory Data Analysis
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline