Data Visualization and BI Dashboards

The chart you put up in the Tuesday review meeting looked clean. The colors matched the brand. Everyone nodded. Three weeks later somebody points out the y-axis started at 78 %, not at zero, so the 2 % drop you'd flagged as "we're holding steady" was the whole visual point of the chart — and the room had read it exactly the way the chart drew it. You didn't mean to mislead anyone. You used the default Excel offered. This course is for the people who put data in front of decision-makers and have realized that the line between a defensible chart and a misleading one is thinner than they thought — and a lot of it lives in defaults nobody set on purpose. Across thirty-eight lessons it teaches the layer underneath the chart-picker menus: the perceptual hierarchy that explains why bars and pies of the same data tell different stories, the data-type and task vocabulary that turns "which chart?" from taste into a procedure, the dashboard archetypes (executive, operational, analytical) that fit different audiences and refresh cadences, and the small catalog of honest-visualization rules — zero baselines for bars, sequential palettes for ordered data, denominators on every rate, a checklist before you ship. It's grounded in the work the practitioners actually cite — Cleveland and McGill's perceptual experiments, Tufte on data-ink, Few on dashboard taxonomy, Knaflic on storytelling, Cairo on how charts lie, ColorBrewer for palettes — and in the BI tools the audience opens every day: Tableau, Power BI, Looker, Metabase. By the end, the chart in the Tuesday review reads the way you meant it to, and the dashboard you ship has a brief, an owner, and a written review checklist that catches the truncated y-axis before someone else does.