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NSW Curriculum
NSW Education Standards Authority

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

Content

Year 12

Data science
Collecting, storing and analysing data
  • Explore the difference between quantitative and qualitative Loading 

  • Determine which Loading  are used to represent quantitative and qualitative data

  • Explore nominal, ordinal, interval and ratio levels of measurement applied to data

  • Investigate Loading , including manual and computerised methods of active and passive data collection

  • Assess the relevance, accuracy, validity and reliability of primary and secondary data

  • Investigate how Loading  supports the development of a deeper understanding of data

  • Interpret and present data using graphs, infographics, dashboards, reports, network diagrams and maps

  • Investigate Loading  and unstructured Loading 

  • Explore the use of likes, emoticons and memes as forms of alternative data as sources of feedback

  • Examine the impact of errors, uncertainty and limitations in data

    Including:
    • data sources
    • raw data versus processed data
    • data bias
  • Explain how Loading  technology is used to manage and verify data

    Including:
    • online voting
    • online identities
    • tracking items of value
    • recordkeeping
  • Examine software features that affect the privacy and security of data

    Including:
    • autofill
    • public or private connections
    • checkbox
    • terms of agreement
  • Explore the use of Loading  and data warehousing, considering volume, variety and velocity

  • Explore the risks and benefits of data mining

  • Analyse the impact of data scale

    Including:
    • volume of raw data
    • storage
    • real-time and continuous streaming
    • opportunities for machine learning (ML)
    • changes in human behaviour
    • ethical implications, including digital footprints
  • Loading  the effectiveness of different methods for data storage

    Including:
    • local storage
    • cloud storage
    • portable storage media
    • data warehouses
Data quality
  • Investigate the ethical use of data for social or Loading  research purposes

  • Explore social, ethical and legal issues associated with using data

    Including:
    • bias
    • accuracy of the collected data
    • metadata
    • copyright and acknowledgement of source data
    • intellectual property and respect for ownership, including Indigenous Cultural and Intellectual Property (ICIP)
    • permissions, rights and privacy of individuals, including cultural responsibility
    • security
  • Investigate the legal issues surrounding data collection and handling

    Including:
    • legislation
    • authorities responsible for data protection
    • data sovereignty of Aboriginal and Torres Strait Islander Peoples
  • Investigate the influence of curated and communicated data on social behaviour

    Including:
    • data literacy
    • timeframes
    • signals impacting on behaviour
    • data swamps
    • educating users
Processing and presenting data
  • Summarise data using a spreadsheet

  • Collate Loading  using spreadsheet analysis features, including charts, statistical analysis and what-if modelling

  • Filter, group and sort data in a spreadsheet to process and display information

    Including:
    • linking multiple sheets to extract data and create summaries
    • applying conditional formatting
    • making data comparisons
    • designing forms and reports
  • Apply spreadsheet analysis features to develop a data dashboard

    Including:
    • graphs
    • pivot tables and slicers
  • Develop a flat-file Loading 

  • Apply Loading  to design a relational database with appropriate user views

    Including:
    • develop a data dictionary
    • linking tables via key fields
    • sort and search data, including using structured query language (SQL)
    • using forms and reports
  • Explore how machine learning and statistical modelling are used in data analytics to analyse big data, and as a prediction tool

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