ITACADEMYprojectes/ProjecteData
May 2025 – Jun 2025
2 Months active
Languages Used
CSVJSONJupyter NotebookMarkdownPythonSQLText
Technical Skills
Chi-squared testData AnalysisData CleaningData EngineeringData ManipulationData Visualization
Raquel Limpo Martínez
PROFILE
Raquel Limpo Martínez
During two months on the ITACADEMYprojectes/ProjecteData repository, Astoreth developed marketing analytics and financial literacy solutions using Python, Pandas, and Jupyter Notebooks. They engineered exploratory data analysis workflows, created reproducible data cleaning pipelines, and delivered visualizations to support campaign optimization and financial segmentation. Their work included stabilizing project data assets, validating onboarding environments, and maintaining metadata hygiene for reliable analytics. By addressing bugs in data handling and improving file management, Astoreth ensured cleaner datasets and more dependable reporting. The depth of their contributions is reflected in robust, well-documented notebooks that enable data-driven decision-making for marketing and financial education initiatives.
Overall Statistics
Feature vs Bugs
65%Features
Repository Contributions
42Total
Bugs
7
Commits
42
Features
13
Lines of code
209,435
Activity Months2
Your Network
36 people
Work History
June 2025
16 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for ITACADEMYprojectes/ProjecteData. Delivered two marketing analytics notebooks with weekday and day-of-month impact, data visualizations, and campaign optimization insights; plus comprehensive financial literacy analytics notebooks with segmentation, knowledge, ownership, and product acquisition trends. Fixed data handling bug in date processing (default year 2008) and performed thorough notebook maintenance to improve reproducibility and cleanliness. Overall, the work strengthened data-driven decisioning for marketing and financial education initiatives, improved data hygiene and reproducibility, and showcased strong Python data science capabilities across analytics, visualization, and metadata governance.
16 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for ITACADEMYprojectes/ProjecteData. Delivered two marketing analytics notebooks with weekday and day-of-month impact, data visualizations, and campaign optimization insights; plus comprehensive financial literacy analytics notebooks with segmentation, knowledge, ownership, and product acquisition trends. Fixed data handling bug in date processing (default year 2008) and performed thorough notebook maintenance to improve reproducibility and cleanliness. Overall, the work strengthened data-driven decisioning for marketing and financial education initiatives, improved data hygiene and reproducibility, and showcased strong Python data science capabilities across analytics, visualization, and metadata governance.
June 2025
May 2025
26 Commits • 11 Features
May 1, 2025May 2025 for ITACADEMYprojectes/ProjecteData focused on delivering core marketing data exploration capabilities, stabilizing project data assets, validating workflows, and solid bug fixes. Key outputs include first exploratory marketing visuals and data treatment, cleanup and stabilization of RLimpo artifacts, a Bash-based environment demo for onboarding, and robust bug fixes that improve reliability. These efforts translated into faster, data-driven marketing insights, cleaner and reproducible datasets, and a more dependable analytics pipeline.
May 2025
26 Commits • 11 Features
May 1, 2025May 2025 for ITACADEMYprojectes/ProjecteData focused on delivering core marketing data exploration capabilities, stabilizing project data assets, validating workflows, and solid bug fixes. Key outputs include first exploratory marketing visuals and data treatment, cleanup and stabilization of RLimpo artifacts, a Bash-based environment demo for onboarding, and robust bug fixes that improve reliability. These efforts translated into faster, data-driven marketing insights, cleaner and reproducible datasets, and a more dependable analytics pipeline.
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.6%
Architecture81.4%
Performance83.8%
AI Usage21.0%
Skills & Technologies
Programming Languages
CSVJSONJupyter NotebookMarkdownPythonSQLText
Technical Skills
Chi-squared testData AnalysisData CleaningData EngineeringData ManipulationData VisualizationExploratory Data AnalysisFile ManagementFinancial LiteracyGitJupyter NotebookJupyter NotebooksMarketing ReportingMarketing StrategyMatplotlib
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