ITACADEMYprojectes/ProjecteData
Apr 2025 – May 2025
2 Months active
Languages Used
CSSCSVHTMLJavaScriptJupyter NotebookMarkdownPower BIPython
Technical Skills
Business IntelligenceCode CleanupCode OrganizationDashDashboard DevelopmentData Analysis
Storkower
PROFILE
Storkower
Alberto Quintana Porras developed analytics and dashboard solutions for the ITACADEMYprojectes/ProjecteData repository over two months, focusing on marketing data pipelines and reporting. He engineered end-to-end data ingestion, cleaning, and transformation workflows using Python and Jupyter Notebooks, integrating exploratory data analysis, mapping, and KPI tracking. Alberto enhanced Power BI dashboards to improve executive reporting and implemented clustering and machine learning workflows to extract marketing insights. He addressed data quality by cleaning encoding artifacts and consolidated data enrichment pipelines for reliable exports. His work demonstrated depth in business intelligence, data engineering, and dashboard development, resulting in faster, more reliable analytics delivery.
Overall Statistics
Feature vs Bugs
85%Features
Repository Contributions
46Total
Bugs
2
Commits
46
Features
11
Lines of code
769,450
Activity Months2
Your Network
36 people
Work History
May 2025
9 Commits • 3 Features
May 1, 2025May 2025 monthly summary for ITACADEMYprojectes/ProjecteData. Focused on delivering analytics capabilities, data quality improvements, and enhanced reporting for 2074. Key progress included marketing analytics enhancements with clustering and ML workflows, encoding cleanup to boost data quality, a consolidated 2074 data enrichment and processing pipeline, and Sprint-04 Power BI reporting for 2074 with updated visuals and data exports. These efforts improved marketing insight generation, data reliability, and executive reporting throughput, enabling faster, data-driven decisions across the business.
9 Commits • 3 Features
May 1, 2025May 2025 monthly summary for ITACADEMYprojectes/ProjecteData. Focused on delivering analytics capabilities, data quality improvements, and enhanced reporting for 2074. Key progress included marketing analytics enhancements with clustering and ML workflows, encoding cleanup to boost data quality, a consolidated 2074 data enrichment and processing pipeline, and Sprint-04 Power BI reporting for 2074 with updated visuals and data exports. These efforts improved marketing insight generation, data reliability, and executive reporting throughput, enabling faster, data-driven decisions across the business.
May 2025
April 2025
37 Commits • 8 Features
Apr 1, 2025April 2025, ITACADEMYprojectes/ProjecteData: Delivered end-to-end data analytics and dashboard enhancements driving clearer marketing insights and more reliable test data provisioning. Highlights include Equip_G test data provisioning to validate data setup, EDA Marketing data ingest/analysis/maps/KPI integration with environment config, and extensive Power BI and Python-based dashboard work across Sprint-01 and Sprint-02. Also advanced S-02/S-03 Marketing analytics notebooks and communications improvements, with a minor code organization refinement. These efforts improved data provisioning reliability, analytics iteration speed, and visibility into marketing KPIs, enabling data-driven decision making. Technologies used include Python, Jupyter notebooks, Power BI (.pbix), and environment variable configurations.
April 2025
37 Commits • 8 Features
Apr 1, 2025April 2025, ITACADEMYprojectes/ProjecteData: Delivered end-to-end data analytics and dashboard enhancements driving clearer marketing insights and more reliable test data provisioning. Highlights include Equip_G test data provisioning to validate data setup, EDA Marketing data ingest/analysis/maps/KPI integration with environment config, and extensive Power BI and Python-based dashboard work across Sprint-01 and Sprint-02. Also advanced S-02/S-03 Marketing analytics notebooks and communications improvements, with a minor code organization refinement. These efforts improved data provisioning reliability, analytics iteration speed, and visibility into marketing KPIs, enabling data-driven decision making. Technologies used include Python, Jupyter notebooks, Power BI (.pbix), and environment variable configurations.
Activity
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Quality Metrics
Correctness83.0%
Maintainability81.8%
Architecture79.6%
Performance77.4%
AI Usage23.0%
Skills & Technologies
Programming Languages
CSSCSVHTMLJavaScriptJupyter NotebookMarkdownPower BIPythonSQL
Technical Skills
Business IntelligenceCode CleanupCode OrganizationDashDashboard DevelopmentData AnalysisData CleaningData EngineeringData EntryData LoadingData ManagementData ProcessingData TransformationData VisualizationDatabase Integration
Repositories Contributed To
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