What a Good Chart Is Supposed to Do
By the end of this module you can say what a specific chart is for, judge whether it earns its space, and recognise the decorative chart that carries no information at all.
Data Visualization and Dashboard Storytelling
An honest dashboard-style story with static charts, statistical visuals, interactive exploration, captions, accessibility notes, and limitations.
Data Questions, Audience, and Decision Context
By the end of this module you can trace a chart back to the decision it supports, and stop making charts for an audience that was never going to act on them.
Chart Anatomy: Axes, Labels, Scales, Legends, and Annotations
By the end of this module you can name every part of a chart and use each deliberately - including the axis that should start at zero and the one that must not.
Comparison Charts: Bars, Slopes, Dot Plots, and Ranking
By the end of this module you can choose the right comparison chart for the question, and explain why a sorted bar chart usually beats the pie chart it replaced.
Time-Series Charts and Trend Explanations
By the end of this module you can plot a time series that shows the trend honestly, and resist the smoothing, truncation and axis choices that make a flat line look like growth.
Distribution Charts and Uncertainty Communication
By the end of this module you can show a distribution rather than hiding it behind an average, and communicate uncertainty without making the chart unreadable.
Relationship Charts: Scatter, Regression, and Grouped Patterns
By the end of this module you can show relationships between variables and describe what a scatter plot does and does not license you to say about cause.
Color, Accessibility, and Visual Hierarchy
By the end of this module you can choose colour that survives colour-blindness and greyscale printing, and use visual hierarchy so the most important thing is seen first.
Matplotlib for Controlled Static Figures
By the end of this module you can build a precise, repeatable static figure in Matplotlib, controlling the details that a default chart gets wrong.
Seaborn for Statistical Exploration
By the end of this module you can use Seaborn to explore a dataset quickly, and know which of its defaults you must override before anyone else sees the result.
Plotly for Interactive Exploration
By the end of this module you can build an interactive Plotly chart, and judge when interactivity genuinely helps rather than hiding the finding behind a hover.
Dashboard Layout, Narrative Flow, and Executive Summary
By the end of this module you can lay out a dashboard that reads in a deliberate order and opens with the answer, rather than making the reader assemble it from tiles.
Capstone: Dashboard Story with Chart Critique and Limitations
In this capstone you will build a dashboard that tells one clear story, critique your own charts against the course's standards, and state the limitations of the data behind them.
First-release boundary
This is a text-first course shell. Leonardo/image creation, image QC, dependency-heavy runtime QA, external review, and learner pilot evidence remain separate later quality steps.