Data visualisation

This lesson comprises eight (8) master classes focusing on:

• Interpreting data
• Visualisation techniques
• User experience (UX) with data
• Data integrity and accuracy
• Trends
• Bias
• Visualisation technologies and tools

Content:

Using data to tell a story

• Explain the purposes of data visualisation, including:
  • simplifying understanding
  • telling a story
  • highlighting significant results
• Describe how features of software contribute to a better understanding of datasets through data visualisation, including spreadsheets, creative design applications and combining applications to track trends and forecast
• Identify patterns in data by interpreting and comparing datasets for an enterprise, social or ethical issue to highlight trends and for predictive data analytics
• Investigate the impact of the evolution of hardware and software on the field of data analytics, including:
  • processing power
  • storage/memory
  • communication media
• Describe online analytical processing (OLAP)
• Assess data integrity in the development of a data visualisation, including:
  • ownership
  • source
  • validation
  • risk
• Explain the impact of enterprise data warehousing on data visualisation, including:
  • analysis and use of historical data trends and patterns
  • correlation with current data
  • data refinement/optimisation
• Explain how big data affects the design and development of data visualisation, including:
  • scope of visible information
  • types and depth of insight provided by the data
• Evaluate bias in data collection, storage and analysis when developing visualisations, including:
  • accuracy
  • audience
  • data sources
  • unconscious bias

Interpreting data visualisations

• Evaluate the effectiveness of software tools used to develop data visualisations, including:
  • spreadsheets used to develop dashboards
  • presentation software used to present data analysis
  • business analytics services, including ‘as a service’ products
  • custom software solutions
• Interrogate data from a data visualisation, including:
  • interpreting what you see
  • aggregation
  • filtering
  • the effect of outliers
  • reasoning

Designing for user experience

• Use graphic design tools to assist in the graphic development of a data visualisation
• Explain how user experience (UX) influences the development of effective data visualisations, including:
  • relevance to the audience
  • audience interpretation
  • customisation
  • live analysis
• Develop and implement criteria for evaluating the effectiveness of user experiences
• Investigate the impact of emerging hardware and software technologies on user interface (UI) and UX design and development

Creating data visualisations

• Research, source, organise and store data appropriate for a data visualisation
• Design and develop a data visualisation for a specific scenario to represent trends, patterns and relationships, and illustrate predictive analysis incorporating big data
• Investigate and implement methods to maintain data security, including:
  • cybersecurity
  • data backup