Tableau Desktop Certified Professional

Explore the difference between dashboard and story in Tableau: dashboard changes propagate to sheets and stories, while stories remain rigid and changes there do not update the dashboard.

Create and arrange story points by dragging sheets into dashboards, setting captions, and applying filters to compare regions and metrics.

Create and arrange story points in Tableau by resizing, fitting to view, and dragging sheets, then annotate and format descriptions to craft polished dashboards.

Explore fixed level of detail calculations in Tableau, learning how fixing a dimension such as category or subcategory affects totals, includes, excludes, and visual results.

Learn to build a radar chart in Tableau by configuring x and y axes, using average values, switching to a continuous month axis, applying a color gradient, and refining formatting.

Explore building a funnel chart in tableau desktop, adjusting axis and layout, handling negative prospect values, and labeling one side while formatting borders and headers for a smooth final visualization.

Learn to create and use bullet charts in Tableau to compare a primary measure with one or more secondary measures and evaluate year-over-year performance.

Explore building bullet charts in Tableau to compare 2014 against 2013 by category and segment, create a performance field, and color-code outperforming and underperforming results across months.

Create horizontal bar graphs of quarterly annualized sales and build a map of market and region sales in Tableau, using drag‑and‑drop, hierarchy drilling, and color changes.

Consolidate all sheets into a fixed-size, professional sales dashboard from the store dataset, arranging treemaps, bar charts, and highlights with color touches and quarter-level filtering.

Learn to build a stacked bar histogram in Tableau that compares male and female visits to resources, label counts, and apply table calculations to reveal gender-based proportions.

Explore forecasting with a daily demand dashboard for a 60-day Brazilian logistics dataset, analyzing day-of-week patterns and sector orders to identify peak demand and forecast orders.