Data Storytelling and Dashboard Design
Turn analysis into a clear message for a real audience: choose and declutter charts, build a narrative that leads to a decision, and design honest, accessible KPI dashboards.
Beginner · Data Science · about 13 hours · 5 modules · certificate on completion
Taught by Keishà Brownè, Ngo Thi Huừngé, AI Learning Companions.
About this course
Analysis only helps when someone understands it and acts on it. This course is about that last step: turning numbers into a message for a particular audience, and into dashboards people can read at a glance. You'll work with two years of figures from Lantern Lane, a made-up online homeware shop whose owners asked a simple question, 'how did 2025 go?', which hides a better story: revenue grew but missed its target, the marketplace boomed, and a website redesign quietly cost hundreds of orders. You'll learn to turn a vague request into a question with a decision behind it, to know your audience, to state your answer as one big idea and to choose the chart that carries it; then to declutter it, use colour only to mean something and write titles that say what the chart shows.
Next you'll build a narrative with situation, complication and resolution, order charts so their titles alone tell the story, and write a one-page summary that leads to a decision. You'll design a KPI dashboard: what it's for, which KPIs deserve a place, how to define them precisely and how to lay them out. The last module covers accessibility (contrast, never colour alone, alternative text) and the charts that mislead without a false number, from truncated axes to dual axes and cumulative lines. Every lab works in Python or a spreadsheet and has exact figures to check, and the capstone is a dashboard, a story and a summary for the shop's owners. It suits analysts, managers and anyone who presents data; Data Visualisation with Matplotlib, Seaborn and Plotly goes deeper into building charts in code.
What you'll learn
- Turn a vague request into a question tied to a decision, and state the answer as one big idea for a named audience
- Choose charts by the comparison they must show, then declutter them, use colour for meaning and write action titles
- Structure a data story as situation, complication and resolution, and present or write it so the audience can act
- Choose, define and lay out KPIs on a one-page dashboard that shows the most important information first
- Make charts accessible and honest, recognising and fixing truncated axes, dual axes and other misleading designs
Skills you'll gain
Data storytelling, Data visualisation, Dashboard design, KPI definition, Chart selection, Decluttering and annotation, Narrative structure, Accessible design, Presenting data
Tools
Python 3, pandas, Matplotlib, Google Sheets, Microsoft Excel, LibreOffice Calc, WebAIM Contrast Checker
Before you start
No coding is needed: the labs give Python code and spreadsheet formulas (Excel, Google Sheets or LibreOffice Calc). For Python, you need basic pandas, as in Data Analysis with Python and pandas, and Python 3 with pandas and Matplotlib. For spreadsheets, SUMIFS and pivot tables, as in Spreadsheet Data Analysis: Formulas, Pivot Tables and Charts. The data comes with the labs.
Modules
From question to message
Turn a vague request into a question with a decision behind it, know exactly who you're telling, and boil the answer down to one sentence and the chart that carries it.
- Exploratory and explanatory: start with the decision (12 min)
- Knowing your audience (12 min)
- The big idea, and the chart that carries it (12 min)
- Hands-on lab: Find the message in a year of shop data
Decluttering, colour and annotation
Strip a chart back to what helps the reader, use colour only where it means something, and write titles and labels that say what the chart shows.
- Decluttering: less ink, less effort (12 min)
- Colour with meaning (12 min)
- Titles, labels and annotation (12 min)
- Hands-on lab: Make over a cluttered chart
Building the narrative
Give a data story a beginning, a middle and an end, order the charts so their titles alone tell the story, and present or write it up so the audience can act.
- Story structure: setup, conflict and resolution (12 min)
- Sequencing charts into a storyline (12 min)
- Presenting and writing it up (12 min)
- Hands-on lab: Build a three-chart story
Dashboards and KPIs
Decide what a dashboard is for, choose and define the few KPIs that matter, and lay them out so the most important information is seen first.
- What a dashboard is for (12 min)
- Choosing and defining KPIs (12 min)
- Laying out a dashboard (12 min)
- Hands-on lab: Design a one-page KPI dashboard
Accessible, honest charts
Make charts and dashboards that everyone can read, recognise and fix the common ways charts mislead, and test your work with real readers before it's shared.
- Designing for accessibility (12 min)
- Misleading charts, and how to avoid them (12 min)
- Testing before you share (10 min)
- Hands-on lab: Fix three misleading charts
Project: Tell the story of Lantern Lane's year, and build its dashboard
Lantern Lane's two owners meet in January to review 2025 and plan 2026, and they've asked for three things. First, a one-page monthly KPI dashboard they can reuse every month: four to six KPIs, each precisely defined and compared with the target or the same month last year, laid out on a grid with the most important information first. Second, a story of five to seven charts, as slides or a document, structured as situation, complication and resolution, whose titles tell the story when read alone; it must cover the channel story and at least one finding from category_sales.csv (revenue by product category). Third, a one-page summary that leads with a recommendation and ends with the decision needed, its owner and a date, stating the assumptions behind any estimate. Use monthly_channels.csv, targets_2025.csv and category_sales.csv from the labs, and work in Python, a spreadsheet or both. Every chart must be accessible (contrast, never colour alone, alternative text) and honest (zero baselines for bars, no dual axes, fair comparisons). Before submitting, test the dashboard and the story with someone who hasn't seen the data, and act on what they say.
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