In Sep 2025, focused on data-quality improvements for cedarcode/mi_carrera, delivering feature enhancements to course prerequisites and subject data accuracy, with OR-based prereqs and subject-code normalization to improve enrollment decisions. No critical bugs fixed this month; effort concentrated on data reliability and consistency. Production data-scraping updates were committed and deployed to keep data current, reducing manual curation and enrollment errors. Key technical achievements include improvements to data modeling, ETL scraping, and version-controlled data updates.

2025-08 Monthly highlights: Implemented Course prerequisites data improvements for cedarcode/mi_carrera. Consolidated updates to scraped subject data to expand prerequisites, defined new subject entries, corrected subject names, and refined credit requirements and dependency rules to strengthen data integrity and downstream subject dependency tracking. Result: higher data quality, more accurate prerequisite calculations, and a foundation for scalable analytics and course-planning features.

July 2025 monthly summary for cedarcode/mi_carrera focusing on data quality improvements to the Course Catalog. Efforts centered on consolidating and maintaining scraped course subjects and prerequisites to ensure accurate, up-to-date information for users, with emphasis on repeatable data maintenance processes and clear commit hygiene.

June 2025 monthly summary for cedarcode/mi_carrera. Focused on data quality improvements for Computational Courses data in the computacion domain, aligning the dataset with current curriculum and downstream analytics. Delivered two targeted updates: a data model/content update for prerequisites and subject definitions, and a correction of course credits in scraped data.

May 2025 monthly summary for cedarcode/mi_carrera. Delivered data enrichment to scraped course subjects by enhancing subject titles with expanded abbreviations and date ranges, improving clarity and accuracy for users seeking detailed course information. This improvement enhances searchability, reduces ambiguity, and supports more reliable analytics for course listings. No major bugs fixed this period. Overall impact: higher quality course data, improved user experience, and a stronger foundation for data-driven decisions. Technologies/skills demonstrated: data scraping enrichment, data asset updates with version control, and stakeholder-focused delivery.

April 2025 centered on refining curriculum data and prerequisites in cedarcode/mi_carrera. Implemented enhancements to the course prerequisite system, specifically updating exam requirements for FÍSICA II and adding an exam requirement to ARQUITECTURA DE COMPUTADORAS. Introduced new subject data for BIOQUÍMICA I and INTRODUCCIÓN A LA VIDA UNIVERSITARIA (IVU) to refine course structure and dependencies. Performed maintenance to ensure data quality: two commits updating scraped subjects data to reflect the latest course information.

March 2025 — cedarcode/mi_carrera: Delivered subject data enrichment for course planning, consolidating updates to scraped subject data to improve accuracy and planning reliability. Implemented complex prerequisite rules, introduced new subjects and codes, refined existing prerequisites, and corrected a data typo to ensure consistent subject information across the catalog. This work enhances course planning quality, supports better advisement, and reduces planning errors across the user base.

February 2025 — CedarCode (cedarcode/mi_carrera) monthly summary focusing on prerequisites data refresh and rule logic updates for course requirements. Delivered data-driven improvements to prerequisite evaluation to reflect updated curriculum.

January 2025: This month focused on data quality and enrollment planning improvements in cedarcode/mi_carrera. Key changes include enhancing course prerequisite logic via updated scraped subjects data and standardizing subject naming to fix data inconsistency. These changes improve enrollment accuracy, planning reliability, and analytics readiness.

December 2024 focused on elevating the quality and reliability of the course catalog data for cedarcode/mi_carrera. Delivered a Course Catalog Subject Data Quality Improvements feature that consolidates updates to scraped subject data, including prerequisite relationships, data corrections (typos, naming conventions), subject group adjustments, and optional subjects refinements to improve catalog accuracy and the data used for scraping subjects. Implemented via four targeted commits to update scraped subjects data, reinforcing data integrity for downstream cataloging and analytics.

2024-11 Monthly summary for cedarcode/mi_carrera: Delivered data-driven enrollment enhancements by enriching subject data and switching prerequisite logic to support 'OR' conditions; no major bugs fixed this month; demonstrates strong data modeling, YAML configuration, and commit-safe data updates with measurable impact on enrollment accuracy and decision quality.

October 2024 — cedarcode/mi_carrera: Delivered Course Catalog Enhancement introducing REDES OPTICAS (5907) with prerequisites; updated validation to incorporate the new course. Updated scraped subjects data to align with catalog changes, ensuring accurate availability and enrollment decisions. No major bugs fixed this month; maintenance focused on data integrity and validation readiness. Business value: improved scheduling accuracy, reduced enrollment friction, and scalable catalog growth. Technologies/skills: backend feature delivery, data modeling, prerequisites validation, data scraping integration, and version control.