visualizing-data_skill

Overview

This skill builds dashboards, reports, and data-driven interfaces that require charts, graphs, or visual analytics. It provides a purpose-first framework to match data characteristics to appropriate visualization types, plus accessibility patterns, colorblind-safe palettes, and performance guidance for different dataset sizes.

How this skill works

Assess data by type (categorical, continuous, temporal, spatial, hierarchical), dimensionality, and point volume, then pick a visualization based on the analytical goal (comparison, trend, distribution, relationship, composition, flow, hierarchy, geographic). It includes a catalog of 24+ chart types organized by purpose, implementation suggestions for JS/TS and Python, WCAG 2.1 AA accessibility rules, and strategies for rendering and aggregation depending on data volume.

When to use it

• Designing dashboards or reports that must communicate clear analytical messages
• Choosing the right chart type based on data shape and analytical purpose
• Creating accessible visualizations that meet WCAG 2.1 AA standards
• Optimizing visualization rendering for datasets from dozens to millions of rows
• Building frontend visualization components with React, D3, Plotly, or Python libraries

Best practices

• Start with data + purpose, not aesthetics; select charts that match the question
• Limit categorical slices (<=6) for pie-like visuals; prefer bar charts for many categories
• Provide text alternatives, strong contrast, and non-color encodings for accessibility
• Switch rendering strategies by data size: SVG <1K, Canvas 10K+, server aggregation >100K
• Avoid dual y-axes and 3D effects; favor small multiples or normalized scales

Example use cases

• Monthly sales dashboard: line charts for trends, small multiples for product comparisons
• Customer segmentation: stacked bars or treemaps to show composition by cohort
• Exploratory analysis: scatter and hexbin plots for relationship detection on large samples
• Network or flow analysis: Sankey or chord diagrams to visualize transfers between groups
• Geospatial reporting: choropleth for regional aggregates, symbol maps for point data