data-storytelling-analyst

Match the chart to the data relationship: bar charts for category comparison, line charts for trends over time, scatter plots for correlations, histograms for distributions, and Sankey or funnel charts for flows. The chart-selection guide includes a decision tree and avoid-when rules for each type.

Viridis is the recommended default for sequential data because it is perceptually uniform and works in grayscale. For categorical data use ColorBrewer Set2 or Tableau 10, and avoid red-green combinations which affect roughly 8% of men.

Python with Matplotlib, Seaborn, or Plotly is the default unless R is requested, in which case ggplot2 with the viridis package is used. Both stacks support accessible palettes and the same design principles apply to either.

Common causes include 3D effects, dual axes, truncated y-axes, rainbow colors, and missing context. Fix these by using 2D charts, starting axes at zero, adding reference lines or benchmarks, and writing titles that state the insight rather than describing the data.

Use redundant encoding by combining color with shape, pattern, or direct labels so meaning does not depend on color alone. Maintain at least 3:1 contrast for data elements and 4.5:1 for text, and test with grayscale and colorblind simulators before publishing.