
Naomi Argüelles developed an end-to-end graph analytics feature for the Fernando-JAL/Neurociencias-2026-1 repository, focusing on data modeling and visualization. She used Python and NetworkX to enable users to define graph structures with node positions and weighted edges, supporting manipulation and analysis of complex networks. Naomi integrated Pandas to convert graph data into DataFrames, streamlining analytics workflows and facilitating further data exploration. Visualization capabilities were implemented using Matplotlib, allowing for immediate graphical insights. The work delivered a foundational capability for network analysis, demonstrating depth in graph theory and data visualization, though the scope was limited to a single feature release.

October 2025: Delivered end-to-end graph analytics capability in Fernando-JAL/Neurociencias-2026-1. Key feature delivers NetworkX-based graph data modeling with nodes (including positions), weighted edges, and manipulation, plus exporting graph data to Pandas DataFrames and visualization for quick insights.
October 2025: Delivered end-to-end graph analytics capability in Fernando-JAL/Neurociencias-2026-1. Key feature delivers NetworkX-based graph data modeling with nodes (including positions), weighted edges, and manipulation, plus exporting graph data to Pandas DataFrames and visualization for quick insights.
Overview of all repositories you've contributed to across your timeline