Skill Profile
Data Visualisation
"The observable action of representing data graphically — choosing the right chart, layout, and visual encoding to communicate patterns and insights clearly."
YOUR SKILLS
Problems This Skill Solves
- Stakeholders unable to interpret raw data tables
- Trends hidden inside thousands of rows of numbers
- Executive decisions being made without clear evidence
- Complex multi-variable relationships that are impossible to explain in words alone
Roles That Use This Skill
1 total · 1 industryThis skill is concentrated in one industry.
Technology / Business / Finance
"The key to good data visualisation is choosing the right chart type for the data."
Chart type selection is a necessary but insufficient decision. The harder choices are what to exclude, how to handle missing data, what comparison the reader actually needs to make, and whether a table would communicate more honestly than a chart. Many visualisations use the correct chart type and still mislead.
"More data points = better visualisation"
Clarity beats completeness. The best visualisations ruthlessly simplify to show one clear thing
Research & Outlook
As data literacy becomes a baseline expectation across all industries, visualisation skills are increasingly valued outside traditional data roles. AI can generate charts, but knowing what to ask for — and whether it's right — remains human.
See This Skill In Action
Watch a professional demonstrate Data Visualisation in a real working environment — what it looks like, how it's applied, and why it matters.
Technical / Creative
Data Visualisation
Also Known As
Growth Path
Can build bar, line, and pie charts in Excel or Google Sheets and label them clearly
Selects the right chart type for the data, uses colour and layout intentionally, builds interactive dashboards
Designs complete data narratives — guiding the viewer's eye to the insight, handling complex multi-dimensional data intuitively
How to Practise
- 1.Take a public dataset (e.g. Our World in Data) and build 5 different chart types from the same data
- 2.Recreate a chart you've seen in a news article from scratch
- 3.Build a live dashboard for something you personally track (fitness, spending, reading)
- 4.Deliberately make a misleading chart, then fix it — understand what you changed and why
How to Prove
- ·Publish a portfolio of dashboards on Tableau Public or GitHub
- ·Complete Google's Data Analytics Certificate (includes visualisation module)
- ·Contribute a data story to a public platform like The Pudding or Observable