The Science of Data Visualisation 2 x 1/2 Days
Learn to use visualisations to summarise insights and communicate information effectively to stakeholders.
Description
- Data visualisation techniques will enable you to use your data for effective communication of complex models, ideas and solutions.
- Improve speed and accuracy of decision-making through visual analysis.
- Understand the science of visual perception to create accurate visual representations.
Learning objectives
- How visuals are hardwired into our biology
- What makes some charts clear and some confusing
- The six simple steps in the visualisation cycle
- How to transform your interaction with decision makers
- Why action must flow from every successful visualisation
Topics
One day course, with four key parts:
Part I - Visualisation: What's the Problem?
- Why is it hard to create good visuals?
- Conversation and language
- Visual perception and cognition
- Truth
- Dataviz Exercises: Basic design
Part II - Essential Tools for the Solution
- Components and attributes
- Scales of measure
- Visual variables
- Chart analysis
- Dataviz Exercises: Meeting objectives
Part III - The Visualisation Process
- Visualisation cycle Mission and quest
- Storytelling and interactivity
- Deconstruction - Olympics Explorer
- Dataviz Exercises: Finding the story
Part IV - Putting it into Practice
- Dataviz Exercises: Innovation
- Whiteboard: Opportunities and challenges
Audience
Analysts who want to understand the fundamental science behind the how to create great data visualisations, why this matters and how they can use these scientific and visualisation techniques to present their results in more compelling ways and influence the audience more effectively.
Course format
Powerpoint presentation to introduce the topics
PDF reader
MS Excel
Group discussion/work
Bring questions from your own work to embed your learning
Related courses
Art of Data Visualisation
Data Visualisation with Tableau
Geospatial Data Visualisation with Tableau
Spatial Data Analysis & Visualisation in R