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Stage 2 | Subject outline | Version control

Digital Technologies Stage 2
Subject outline

Version 4.0 - For teaching in 2024.
Accredited in November 2016 for teaching at Stage 2 from 2019.

Stage 2 | Subject outline | Content | Focus area 3: Data analytics

Focus area 3: Data analytics

Students use a range of data‑collection tools, techniques, and methods to collect and/or source data. They determine the appropriate data collection method for a project.

Students apply computational thinking skills to collect, extract, interpret, and model data sets. They analyse data sets to identify social, economic, environmental, scientific, and/or other trends. These trends provide a source of information from which students can explore and test hypotheses to determine solutions to issues, for example, in business, industry, the environment, and the community. Such issues may be of local, national, and/or global importance.

Students analyse data sets to find simple relationships, which are generally linear. They may also analyse more complex relationships, which may be, for example, exponential or logarithmic.

Students study the ethical implications of data collection, storage, sharing, use, and security. They examine privacy issues and the role of government, business, and society in determining cybersecurity policy. Students extend their critical understanding of the appropriate and ethical uses of digital technology.

Self‑assessment tools or skills frameworks may be used to support the development and application of students’ skills in working collaboratively.

The following framework provides a set of possible techniques and strategies that can be used for learning.

Key learning elaboration
Possible techniques and strategies
Sources of data

Students identify trends, for example, social, economic, environmental, scientific, and other trends, through extracting, interpreting, and modelling data sets.



Students explore a research question or hypothesis.

Students design and conduct surveys.

Students extract data from online and public data sets.

Students make use of digital technologies to collect data; for example, video cameras, wearable technology, sensors (e.g. light, body heat, and movement), GPS tracking, and dynamic and static screen-capture.

Students check data integrity including: currency, authenticity, relevance, accuracy, and outliers (cleaning).

Analysis

Students analyse data sets to inform thinking about issues related to trends.

Students analyse data sets to find simple relationships, which are generally linear. They may also analyse more complex relationships, which may be, for example, exponential or logarithmic.
 



Students investigate characteristics of a data set, such as shape, through visualisation, using a range of tools.

Students test a research question or hypothesis.


Students recognise patterns and relationships in data.

Students use simple quantitative techniques to analyse data and tabulate results.

Students explore examples of how data analytics are used, for example, in business, health, science, and government to support decision‑making.
Ethical considerations

Students evaluate the ethical implications surrounding the collection, storage, use, and/or security of data.



Students evaluate strategies for use by government, business, organisations, and individuals for:

  • collection of personal data
  • data security and storage
  • data protection and backup
  • privacy and anonymising data
  • cyber security.