
During two months on the professor-jon-white/COSC_352_FALL_2025 repository, Dajoh99 developed and modernized data extraction and analytics workflows. They built a Python-based web scraping toolkit to extract HTML tables from URLs, saving results as CSV files with robust error handling and Docker-based containerization for reproducibility. Dajoh99 also overhauled project setup, refreshed datasets, and introduced environment-configurable extraction pipelines. In October, they migrated analytics to a Scala-based Docker environment and delivered a homicide data analysis tool that outputs categorized results in multiple formats. Their work demonstrated depth in Python, Scala, Docker, and scripting, enabling maintainable, scalable, and reliable data pipelines.

October 2025 monthly delivery focused on modernizing the data extraction and analytics stack, enabling broader table coverage, more robust outputs, and scalable analytics deployment. These changes improve data availability, deployment reliability, and business-ready insights.
October 2025 monthly delivery focused on modernizing the data extraction and analytics stack, enabling broader table coverage, more robust outputs, and scalable analytics deployment. These changes improve data availability, deployment reliability, and business-ready insights.
September 2025 monthly summary for professor-jon-white/COSC_352_FALL_2025: Delivered two features focused on data collection, maintainability, and containerized execution. Feature 1: Web Scraping Toolkit to extract HTML tables with class 'wikitable' from a URL and save as CSV with robust error handling; added Docker support for containerized execution and environment setup. Feature 2: Project Setup and Dataset Refresh to reorganize project structure, initialize dependencies (requirements.txt), and update dataset content by replacing the programming-language CSV with anime episodes data; Docker Compose teardown completed. No critical bugs reported this month. Impact: enables reliable, repeatable data pipelines for coursework and research, reduces onboarding and setup time, and improves deployment consistency. Technologies demonstrated: Python, HTML parsing, CSV handling, Docker, Docker Compose, and project scaffolding.
September 2025 monthly summary for professor-jon-white/COSC_352_FALL_2025: Delivered two features focused on data collection, maintainability, and containerized execution. Feature 1: Web Scraping Toolkit to extract HTML tables with class 'wikitable' from a URL and save as CSV with robust error handling; added Docker support for containerized execution and environment setup. Feature 2: Project Setup and Dataset Refresh to reorganize project structure, initialize dependencies (requirements.txt), and update dataset content by replacing the programming-language CSV with anime episodes data; Docker Compose teardown completed. No critical bugs reported this month. Impact: enables reliable, repeatable data pipelines for coursework and research, reduces onboarding and setup time, and improves deployment consistency. Technologies demonstrated: Python, HTML parsing, CSV handling, Docker, Docker Compose, and project scaffolding.
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