Course Leader | Thomas Bjørnskau (Statistics Norway) |
| Target Group | ESS staff with knowledge of data visualisation and involved in dissemination production of visualisation and/or consulting. |
| Entry Qualifications | Sound command of English. Participants should be able to make short interventions and to actively participate in discussions Interest for and some experience in data visualisation in statistical dissemination Some production of either infographics, interactive data visualisation, advanced statistical charts and diagrams, statistical maps, or interactive dashboards Must be able and willing to present their own visualisation/case for 5–10 minutes during the course
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| Objective(s) | Equip participants with in-depth knowledge and practical skills in data visualisation, enabling them to design and evaluate visual representations of data tailored to different contexts and audiences Participants will learn advanced techniques for handling complex data, creating narratives, and incorporating design principles that make visualisations both informative and engaging
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| Contents | In-depth Review of Different Chart Types: From line charts, bar charts, area charts, scatter plots, and pie charts to Sankey diagrams, tree maps, dot plots, heatmaps, pictograms, etc Contextualization and Audience: How to choose the right visualisation based on the nature of the data and the audience you want to reach Storytelling with Visuals: How to use visualisations to tell stories and convey insights from the data Design Principles: Advanced techniques for creating more sophisticated and informative visualisations, including the use of interactivity, hierarchical information, and multidimensional data Common Mistakes and Pitfalls in Data Visualisation: Review of examples of data distortion, lack of context, excessive information, misrepresenting uncertainty, etc Practical Examples: Review of successful data visualisations and discussions on what makes them effective
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| Expected Outcome | Critically evaluate their own visualisations, identifying areas for improvement based on course principles Apply storytelling and design techniques to make their specific datasets and statistical topics more accessible and impactful for their audiences Detect and address potential biases or errors in their visualisations, ensuring accuracy and credibility in their specific statistical context
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| Training Methods | Presentations and lectures Hands-on production, sketching and/or prototyping Exchange of views/experiences on national practices
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Required Reading | "Data visualisation literacy: Definitions, conceptual frameworks, exercises, and assessments" (Katy Börner, Andreas Bueckle, and Michael Ginda) |
| Suggested Reading | |
| Required Preparation | Each participant should bring a visualisation of their own production to reflect best practices and lessons learned Each participant should bring a statistical dataset of their own choice to work out some practical examples
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Trainer(s)/
Lecturer(s) | Trainers: Thomas Bjørnskau (Statistics Norway) Lecturers: TBD, 1-3 statisticians from Statistics Norway |
