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
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.
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.
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.
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.
