Data Visualisation

7 dot points across 4 inquiry questions, in syllabus order. Each dot point has a focused answer, with exam-style questions and worked answers where available.

Using data to tell a story

Explain the purposes of data visualisation, including simplifying understanding, telling a story and highlighting significant results; describe how features of software contribute to a better understanding of datasets through data visualisation, including spreadsheets, creative design applications and combining applications to track trends and forecast; identify patterns in data by interpreting and comparing datasets for an enterprise, social or ethical issue to highlight trends and for predictive data analytics

The three purposes of data visualisation (simplifying understanding, telling a story, highlighting significant results), how spreadsheets, creative design applications and combined tools help track trends and forecast, and how to compare datasets to identify patterns and support predictive analytics.

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Investigate the impact of the evolution of hardware and software on the field of data analytics, including processing power, storage/memory and communication media; describe online analytical processing (OLAP)

How growth in processing power, storage and memory, and communication media transformed data analytics from overnight batch reports to real-time interactive dashboards, and what online analytical processing (OLAP) is: data cubes, dimensions, measures, and roll-up, drill-down, slice, dice and pivot.

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Assess data integrity in the development of a data visualisation, including ownership, source, validation and 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 and data refinement/optimisation; explain how big data affects the design and development of data visualisation, including scope of visible information and types and depth of insight provided by the data

How to assess the integrity of data behind a visualisation (ownership, source, validation, risk), how an enterprise data warehouse improves visualisation through historical trends, correlation with current data and refinement, and how big data changes the scope and depth of what a visualisation can show.

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Evaluate bias in data collection, storage and analysis when developing visualisations, including accuracy, audience, data source and unconscious bias

How bias enters a visualisation at the collection, storage and analysis stages, evaluated through accuracy, audience, data source and unconscious bias, plus the design tricks that mislead (truncated axes, cherry-picked ranges, 3D and dual axes) and how to reduce bias.

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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 and data backup

The process for building a data visualisation for a scenario: researching and sourcing data, cleaning, organising and storing it, designing and developing visuals that show trends, patterns, relationships and predictions from big data, and protecting the data with cybersecurity controls and backups.

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Designing for user experience

Use graphic design tools to assist in the graphic development of a data visualisation; explain how user experience (UX) influences the development of effective data visualisations, including relevance to the audience, audience interpretation, customisation and live analysis; develop and implement criteria for evaluating the effectiveness of user experiences; investigate the impact of emerging hardware and software technologies on user interface (UI) and UX design and development

How graphic design tools and UX thinking make data visualisations effective: relevance to the audience, audience interpretation, customisation and live analysis; how to develop and apply criteria to evaluate UX; and how emerging hardware and software are changing UI and UX design.

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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, and custom software solutions; interrogate data from a data visualisation, including interpreting what you see, aggregation, filtering, the effect of outliers and reasoning

How to evaluate spreadsheets, presentation software, business analytics services (including 'as a service' products) and custom software for building visualisations, and how to interrogate a visualisation by interpreting it, questioning aggregation and filtering, checking outliers and reasoning carefully.

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