
Worked on the professor-jon-white/COSC_352_FALL_2025 repository to deliver five features over two months, focusing on data extraction, analytics, and deployment reliability. Developed a Python-based web scraping toolkit to extract HTML tables and output them as CSV, with robust error handling and Docker-based containerization for reproducible workflows. Modernized the table extraction pipeline to support broader table coverage and configurable outputs, and introduced a Scala-based analytics tool for homicide data analysis with flexible output formats. Emphasized maintainability by reorganizing project structure, upgrading Docker and Scala runtimes, and streamlining orchestration scripts, demonstrating skills in Python, Scala, Docker, and data manipulation.
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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