
Chinonso Egeolu developed automated data extraction tools for the professor-jon-white/COSC_352_FALL_2025 repository, focusing on converting HTML tables to CSV files and streamlining data workflows. Over two months, Chinonso delivered a Python-based converter and a Bash-driven pipeline, both containerized with Docker to ensure reproducibility across environments. The solutions included robust file management, error handling, and automated packaging, supporting scalable data processing for course needs. By reorganizing the repository structure and enhancing documentation, Chinonso improved maintainability and onboarding. The work demonstrated depth in Python development, shell scripting, and Docker, resulting in a reliable foundation for ongoing data pipeline enhancements.

October 2025 (COSC_352_FALL_2025): Delivered an automated webpage-to-CSV pipeline leveraging a Bash script and a Docker-based table extraction workflow. The solution downloads HTML content, runs a containerized table extractor to generate CSVs, assigns unique output names, and packages results as a ZIP for distribution. Implemented robust error handling for missing arguments and Docker image build steps. No major bugs were reported this month; focus remained on delivering a repeatable data-processing primitive that scales across environments and supports the course's data pipeline needs.
October 2025 (COSC_352_FALL_2025): Delivered an automated webpage-to-CSV pipeline leveraging a Bash script and a Docker-based table extraction workflow. The solution downloads HTML content, runs a containerized table extractor to generate CSVs, assigns unique output names, and packages results as a ZIP for distribution. Implemented robust error handling for missing arguments and Docker image build steps. No major bugs were reported this month; focus remained on delivering a repeatable data-processing primitive that scales across environments and supports the course's data pipeline needs.
In Sep 2025, delivered a containerized HTML table to CSV converter for the COSC_352_FALL_2025 project, with substantial repository structure and documentation improvements. The work enables reproducible data extraction workflows, easier onboarding, and a clearer path for future enhancements. Key outcomes include a Python-based converter, sample inputs/CSV data, README coverage, and Docker support to run the tool in a consistent environment. Additionally, repository hygiene improvements reduce maintenance overhead and branch confusion, setting a solid baseline for ongoing course tooling.
In Sep 2025, delivered a containerized HTML table to CSV converter for the COSC_352_FALL_2025 project, with substantial repository structure and documentation improvements. The work enables reproducible data extraction workflows, easier onboarding, and a clearer path for future enhancements. Key outcomes include a Python-based converter, sample inputs/CSV data, README coverage, and Docker support to run the tool in a consistent environment. Additionally, repository hygiene improvements reduce maintenance overhead and branch confusion, setting a solid baseline for ongoing course tooling.
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