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11–12Enterprise Computing 11–12 Syllabus (2022)

Record of changes
OverviewRationaleAimOutcomesContentAssessmentGlossaryTeaching and learning support

Content

Year 12

Data visualisation
Using data to tell a story

  • Explain the purposes of Loading  Loading 

    Including:
    • simplifying understanding
    • telling a story
    • highlighting significant results

  • Describe how features of software contribute to a better understanding of Loading  through data visualisation, including spreadsheets, creative design Loading  and combining applications to track trends and forecast

  • Identify patterns in data by interpreting and comparing datasets for an Loading , social or ethical issue to highlight trends and for predictive data analytics

  • Investigate the impact of the evolution of Loading  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 Loading  affects the design and development of data visualisation

    Including:
    • scope of visible information
    • types and depth of insight provided by the data

  • Loading  bias in data collection, storage and analysis when developing visualisations

    Including:
    • accuracy
    • audience
    • data source
    • 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 Loading  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 Loading  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
Related files