TheAnonymous-stack/numi-scraper
Jul 2025 – Jul 2025
1 Month active
Languages Used
JSONPython
Technical Skills
Content CreationData EngineeringData ExtractionPythonWeb Scraping
Nina Low
PROFILE
Nina Low
During July 2025, Z. Low developed and integrated a Math Problems Dataset for Grades 4 and 8 within the TheAnonymous-stack/numi-scraper repository, delivering two JSON files covering number sense, fractions, decimals, geometry, and financial literacy for an educational platform. They also addressed a critical bug in the scraper’s tag and question number handling, improving the reliability and accuracy of data extraction. Using Python and web scraping techniques, Z. Low’s work enhanced the platform’s data quality and reduced manual curation needs. The technical depth is evident in the robust data engineering approach and version-controlled, traceable improvements to the scraping pipeline.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
5,411
Activity Months1
Your Network
19 people
Work History
July 2025
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary for 2025-07: Key features delivered include the Math Problems Dataset for Grades 4 and 8 and a major scraper reliability fix. The dataset adds two JSON files with problems across number sense, fractions, decimals, geometry, and financial literacy for use in the educational platform. The scraper bug fix—Robust Tag and Question Number Handling in Scraper—resolves inconsistencies in tag and question number processing, improving data extraction reliability and accuracy. Overall impact: higher quality data feeds for the educational platform, reduced manual data curation, and faster content provisioning. Technologies demonstrated: data extraction reliability, JSON dataset creation, and version-control-traceable changes in a scraping pipeline.
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary for 2025-07: Key features delivered include the Math Problems Dataset for Grades 4 and 8 and a major scraper reliability fix. The dataset adds two JSON files with problems across number sense, fractions, decimals, geometry, and financial literacy for use in the educational platform. The scraper bug fix—Robust Tag and Question Number Handling in Scraper—resolves inconsistencies in tag and question number processing, improving data extraction reliability and accuracy. Overall impact: higher quality data feeds for the educational platform, reduced manual data curation, and faster content provisioning. Technologies demonstrated: data extraction reliability, JSON dataset creation, and version-control-traceable changes in a scraping pipeline.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
Content CreationData EngineeringData ExtractionPythonWeb Scraping
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