Data visualisation

Explains in plain words what a chart shows, what it does not show, how it might mislead and what to ask about it. Use when you are handed a chart in the news, a report or a meeting.

Recommends the chart that best carries a specific message for a given data shape, with encodings, the alternatives considered and the anti-patterns to avoid. Use before building a chart.

Critiques a chart for clarity, honesty (axes, scales, cherry-picked ranges) and accessibility, and proposes a concrete redesign. Use before a chart goes into a deck, report or dashboard.

Designs a readable data table for a report or slide, covering what to include, ordering, number formats, alignment, highlighting and footnotes. Use when your tables get skipped or misread.

Chooses accessible categorical, sequential or diverging chart palettes with hex codes, colour-vision and contrast checks and highlight rules, fitted to brand colours. Use when colouring charts.

Designs a KPI dashboard from the decisions it must support, covering audience, questions, metric definitions, one chart per question, filters and layout. Use before building it in a BI tool.

Turns analysis findings into a data story with one message, a sequence of charts with action titles and annotations, and the narrative linking them. Use when presenting to non-analysts.

Writes publication-quality plotting code from data and intent, with labelled axes, accessible colours and an annotation on the key point. Use for matplotlib, seaborn, plotly, ggplot2 or Vega-Lite.