11–12Enterprise Computing 11–12 Syllabus (2022)
Content
Year 12
- 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
 
- 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
 
- 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