Data Awareness

This section covers the fundamentals of data visualisation and provides a crash course on the foundational elements that give you the vocabulary you need to explore and understand the scenarios.

Why Do We Visualise Data?

Visualisation reveals hidden insights:

Charts expose patterns and trends that tables or statics alone can obscure.

Supports memory and comprehension:

Visuals help us retain and compare information more effectively than raw numbers.

Preattentive attributes matter:

Colour, size, and position allow the brain to instantly detect differences.

Aggregation is key:

Encoding frequencies and comparisons (e.g. bar charts) transforms raw data into clear insights.

Data Types:

Categorical, Ordinal, Quantitative (discrete or continuous).

Chart encoding:

Position, colour, and aggregation are used to represent different data types.

Colour Use:

Sequential, diverging, categorical, highlighted, and alerting schemes help convey meaning and emphasis.

Accessibility:

Colour-bind friendly palettes (e.g. blue-orange) ensure dashboards remain clear for all viewers.

Types Of Data

Common Chart Types

Core charts:

Bar, line scatter, dot, maps, tables/highlight tables and bullet graphs cover most dashboard needs.

Strengths:

Bar charts excel at comparisons, line charts show trends, scatterplots reveal relationships, and bullet graphs highlight actual vs target.

Limitations:

Pie chats and circle sizes are harder to interpret accurately; missing too many visual attributes reduces clarity.

Guiding Principle:

Every chart emphasizes some feature while obscuring others - choose the chart that best answers the specific analytical question.