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Learn how to transform data into clear, accurate and compelling visual insights. This two-day course introduces the principles of visual analytics, data visualisation and evidence-based storytelling. Participants learn how to select suitable charts, structure visual information, design effective dashboards and communicate analytical findings to different audiences. The course combines principles of visual perception with practical approaches to exploratory and explanatory analysis. It also examines advanced chart types, common visualisation errors and the ethical responsibilities involved in presenting data.
You will learn how to analyse data visually and communicate findings through charts, dashboards and infographics. You will evaluate the purpose, audience and structure of a visualisation, select appropriate formats and refine visual displays to improve clarity and impact.
• Understand the role of visual analytics within data science and decision-making
• Apply essential principles of effective data visualisation
• Select charts based on the data, analytical question and audience
• Use visual perception and Gestalt principles to structure information
• Design and refine advanced charts without unnecessary complexity
• Distinguish exploratory analysis from explanatory communication
• Build coherent dashboards and visual narratives
• Identify misleading, inaccurate and ineffective visualisations
• Communicate analytical findings clearly and responsibly
• Basic experience working with data in spreadsheets, reporting tools or analytical software
• Familiarity with simple tables and charts
• Basic understanding of descriptive statistics is recommended
• No advanced programming or data science experience is required
• Participants may use an agreed visualisation tool appropriate to their organisation
*We customize the course outline and content to your specific needs and relevant use cases.
Day 1: Visual analysis and effective chart design
Module 1: Understanding visual analytics
• Defining visual analytics, data visualisation and visual data exploration
• Understanding the role of visualisation within the data science workflow
• Applying five core principles of effective data visualisation
• Comparing the strengths and limitations of tables and charts
Module 2: Visual perception and information design
• Understanding how people perceive patterns, differences and relationships
• Applying Gestalt principles of proximity, similarity, connection and enclosure
• Using position, length, size, colour and shape to encode data
• Creating clear visual hierarchy and directing audience attention
Module 3: Selecting effective visualisations
• Matching charts to comparisons, distributions, trends and relationships
• Selecting formats for categorical, numerical, temporal and geographical data
• Choosing visualisations according to audience and communication purpose
• Recognising when a table or simple indicator is more effective than a chart
Module 4: Advanced charting and visual refinement
• Understanding the uses and limitations of treemaps, Sankey diagrams and radar charts
• Working with heat maps, small multiples and other advanced displays
• Removing clutter, unnecessary decoration and redundant labels
• Improving scales, legends, annotations and visual consistency
Day 2: Data storytelling, dashboards and responsible communication
Module 5: Exploratory and explanatory analysis
• Using visualisation to investigate data and generate questions
• Moving from exploratory findings to an explanatory presentation
• Identifying relevant patterns, exceptions and uncertainty
• Selecting the evidence needed to support an analytical conclusion
Module 6: Narrating with data
• Structuring a clear data story around audience needs
• Establishing context, analytical questions and key messages
• Highlighting important findings through emphasis and annotation
• Combining charts, explanatory text and recommendations coherently
Module 7: Dashboards and infographics
• Defining the purpose, users and decisions supported by a dashboard
• Organising indicators and charts into a clear information hierarchy
• Designing layouts for scanning, comparison and focused analysis
• Applying filters, interactions and navigation without overwhelming users
• Distinguishing analytical dashboards from explanatory infographics
Module 8: Accuracy, ethics and applied visual analytics
• Recognising distorted axes, inappropriate scales and misleading comparisons
• Avoiding excessive aggregation, selective presentation and visual bias
• Representing missing data, uncertainty and limitations honestly
• Reviewing real-world examples of effective and ineffective visual analytics
• Developing and evaluating a complete visual analysis for a selected data scenario
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