
Edicion developed a comprehensive data analytics and visualization suite for the ITACADEMYprojectes/ProjecteData repository, focusing on client data exploration and business intelligence readiness. Over two months, Edicion enhanced repository hygiene, standardized data cleaning scripts, and implemented robust exploratory data analysis with KPIs, heatmaps, and radar charts. Using Python, Pandas, and Plotly, Edicion improved data quality through imputation and unified file structures, enabling reliable reporting and model training. The work included predictive analytics with scikit-learn, automated reporting in Jupyter Notebooks, and secure credential management. These efforts resulted in a maintainable codebase and deeper, actionable insights for stakeholder decision-making.

May 2025 monthly summary for ITACADEMYprojectes/ProjecteData: Delivered a data analytics and visualization suite focused on Clientes, enabling richer insights, ML-enabled analysis, and BI readiness. Implemented end-to-end data storytelling with robust visualizations (EDA, KPIs) and documented analyses to facilitate stakeholder interpretation. Achieved data quality gains through file unification and weekly CSV cleaning, improving reliability for reporting and model training. Established BI-ready output through Power BI exports (CSV and dataframe) and created occupancy analytics visuals to support capacity planning. Stabilized weekly scripts and automated reporting (PDFs) for weeks 2 and 3, accelerating delivery cycles and enabling faster decision-making.
May 2025 monthly summary for ITACADEMYprojectes/ProjecteData: Delivered a data analytics and visualization suite focused on Clientes, enabling richer insights, ML-enabled analysis, and BI readiness. Implemented end-to-end data storytelling with robust visualizations (EDA, KPIs) and documented analyses to facilitate stakeholder interpretation. Achieved data quality gains through file unification and weekly CSV cleaning, improving reliability for reporting and model training. Established BI-ready output through Power BI exports (CSV and dataframe) and created occupancy analytics visuals to support capacity planning. Stabilized weekly scripts and automated reporting (PDFs) for weeks 2 and 3, accelerating delivery cycles and enabling faster decision-making.
April 2025: Delivered tangible business value through repository hygiene, expanded client analytics, and strengthened data governance for the ITACADEMYprojectes/ProjecteData repository. Key outcomes include: repository hygiene hardened (global .gitignore enforcement; removal of Mac DS_Store and test files) reducing noise and risk in releases; date formatting standardized in cleaning scripts to ensure consistent downstream processing; expanded EDA for Clientes with KPIs, heatmaps, and radar charts delivering richer, actionable insights; plotting improvements and enhanced client charts via Plotly KPI visualizations; security and data handling strengthened by hiding credentials in .env and preventing credential leakage in logs; data quality improvements through imputations for missing values across ratings, evaluations, and reviews; groundwork laid for Sprint 02 clientes with scaffolding and scripts; nomenclature unified across project; and bug fixes addressing duplicate client charts, Week 2 CSV restoration, and push-imputation corrections. Overall impact: safer, more maintainable codebase with deeper, data-driven client insights enabling faster, confident decision-making and smoother future iterations.
April 2025: Delivered tangible business value through repository hygiene, expanded client analytics, and strengthened data governance for the ITACADEMYprojectes/ProjecteData repository. Key outcomes include: repository hygiene hardened (global .gitignore enforcement; removal of Mac DS_Store and test files) reducing noise and risk in releases; date formatting standardized in cleaning scripts to ensure consistent downstream processing; expanded EDA for Clientes with KPIs, heatmaps, and radar charts delivering richer, actionable insights; plotting improvements and enhanced client charts via Plotly KPI visualizations; security and data handling strengthened by hiding credentials in .env and preventing credential leakage in logs; data quality improvements through imputations for missing values across ratings, evaluations, and reviews; groundwork laid for Sprint 02 clientes with scaffolding and scripts; nomenclature unified across project; and bug fixes addressing duplicate client charts, Week 2 CSV restoration, and push-imputation corrections. Overall impact: safer, more maintainable codebase with deeper, data-driven client insights enabling faster, confident decision-making and smoother future iterations.
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