d2cml-ai/Data-Science-Python
Apr 2025 – Jun 2025
2 Months active
Languages Used
CSSCSVHTMLMarkdownPythonSQLNoneText
Technical Skills
API IntegrationCSV HandlingData AnalysisData CollectionData EngineeringData Extraction
Hide801
PROFILE
Hide801
Contributed to the d2cml-ai/Data-Science-Python repository by developing automated data pipelines and educational resources over a two-month period. Built a Selenium-based job postings scraper and a Codeforces contest data processor, both exporting enriched datasets to CSV for analysis using Python and pandas. Established Google Drive integration for homework dataset access and managed notebook execution states. Expanded geospatial workflows by creating a Jupyter Notebook for protected area data, leveraging OpenStreetMap and MODIS VCF data with geopandas and rasterio for geocoding and export to GeoPackage and GeoTIFF. Improved repository hygiene and documentation, supporting reproducible data science and machine learning education.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
5
Lines of code
9,936
Activity Months2
Your Network
21 peopleWork History
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for d2cml-ai/Data-Science-Python: Key features delivered, major fixes, and impact across geospatial data processing and ML education resources. Focused on enabling end-to-end data workflows for protected-area data, expanding ML learning materials, and improving repository hygiene. Highlights include a new Data Processing Notebook (HW4.ipynb) with OpenStreetMap and MODIS VCF data handling and export to GeoPackage/GeoTIFF, updated coursework resources with Colab links for Homework #112 and materials for Homework #6, a GPU-focused performance and challenges report, and cleanup of a stray temporary Word file; all work tracked with clear commits for traceability.
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for d2cml-ai/Data-Science-Python: Key features delivered, major fixes, and impact across geospatial data processing and ML education resources. Focused on enabling end-to-end data workflows for protected-area data, expanding ML learning materials, and improving repository hygiene. Highlights include a new Data Processing Notebook (HW4.ipynb) with OpenStreetMap and MODIS VCF data handling and export to GeoPackage/GeoTIFF, updated coursework resources with Colab links for Homework #112 and materials for Homework #6, a GPU-focused performance and challenges report, and cleanup of a stray temporary Word file; all work tracked with clear commits for traceability.
June 2025
April 2025
4 Commits • 3 Features
Apr 1, 20252025-04 monthly summary for d2cml-ai/Data-Science-Python: Key features delivered, major fixes, overall impact, and skills demonstrated. This month focused on expanding automated data collection, enriching datasets, and establishing end-to-end data pipelines to accelerate analysis and decision-making. Major bugs fixed: none reported; minor reliability tweaks in ingestion notebooks to accommodate updated data structures. Overall impact and accomplishments: increased data availability and data quality across job postings and coding contest datasets, enabling faster insights and repeatable workflows. Technologies demonstrated: Selenium-based scraping, Python data pipelines, API integration, pandas DataFrames, Google Drive data access, notebook execution state management, CSV I/O, and data orchestration.
April 2025
4 Commits • 3 Features
Apr 1, 20252025-04 monthly summary for d2cml-ai/Data-Science-Python: Key features delivered, major fixes, overall impact, and skills demonstrated. This month focused on expanding automated data collection, enriching datasets, and establishing end-to-end data pipelines to accelerate analysis and decision-making. Major bugs fixed: none reported; minor reliability tweaks in ingestion notebooks to accommodate updated data structures. Overall impact and accomplishments: increased data availability and data quality across job postings and coding contest datasets, enabling faster insights and repeatable workflows. Technologies demonstrated: Selenium-based scraping, Python data pipelines, API integration, pandas DataFrames, Google Drive data access, notebook execution state management, CSV I/O, and data orchestration.
Activity
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Quality Metrics
Correctness80.0%
Maintainability82.2%
Architecture75.6%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSCSVHTMLMarkdownNonePythonSQLText
Technical Skills
API IntegrationAssignment SubmissionCSV HandlingData AnalysisData CollectionData EngineeringData ExtractionData ProcessingData ScienceDocumentationGPU ComputingGeospatial AnalysisJupyter NotebookJupyter NotebooksMODIS Data Processing
Repositories Contributed To
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