Data Visualization

What the unit covers

The resources run from first principles to hands-on code. The Data Visualization Notes define the subject and set out its characteristics, types of data, Bertin's visual variables, Mackinlay's design criteria and the stages of the process. The mind map called Data visualization lays out the basic charts: bar, column, histogram, line, pie, scatter, dot and box plots.

Where to start

Begin with the notes, then the Data visualization mind map, so the vocabulary is in place. The Which chart do I use?! map and the Visual Vocabulary flashcards help you match a chart to the question you are asking. The Data Visualization flashcards cover the visualization pipeline, the composition of colour, luminance, Gestalt principles of grouping and the four data types.

Going deeper

• Theme 3: Visualization looks at how visual representations support individual sensemaking when exploring data.
• Networks and trees covers link marks, force-directed placement and matrix views.
• Further maps cover data storytelling, dashboards, EDA with two variables and turning data into video.

Learning to code the charts

The slide set on Seaborn explains that it is built on top of Matplotlib and is meant to complement it. The Seaborn exercises flashcards use the well-known Titanic data set, and the notes on data analysis and visualisation using Python give a wider overview. There is also a mind map on seaborn.distplot.

Using quizzes and maps together

Try the quiz titled Data Visualization, where you fill in blanks such as data-ink ratio and Edward R. Tufte. Then take the Data Visualization MIDTERM quiz, which asks you to rank visual encodings by how effectively they let a reader perceive a value. The Visualization Study quiz asks you to read graphs and judge which is clearer. After each attempt, return to the matching map to see what you missed.

This unit sits in the Data Science and Analytics strand of Databases and data science. Pick one quiz now and see where you stand.