Content:
Collecting, storing and analysing data
- Explore the difference between quantitative and qualitative data
- Determine which data types are used to represent quantitative and qualitative data
- Explore nominal, ordinal, interval and ratio levels of measurement applied to data
- Investigate data sampling, 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 informatics supports the development of a deeper understanding of data
- Interpret and present data using graphs, infographics, dashboards, reports, network diagrams and maps
- Investigate structured and unstructured datasets
- 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 blockchain 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 big data 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
- Evaluate 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 enterprise 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 information 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 database
- Apply computational thinking 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
