Visualisation

Question 1

Data visualisation

Answer 1

Graphical representation of data to convey complex information, patterns, or insights in a clear and easily understandable way

Question 2

Visual encodings

Answer 2

How we map data to graphical elements - position, colour, shape, etc.
When we design a data visualisation, we want to use as few visual encodings as possible

Question 3

Perceptual hierarchy of visual encodings

Answer 3

Tells us which ones are the easiest for humans to read

When choosing the right chart, the primary goal is to prioritise visual encodings that are higher on the perceptual hierarchy

Question 4

Primary goal when choosing the right chart

Answer 4

Prioritise visual encodings that are higher on the perceptual hierarchy e.g. length as opposed to area, volume or colour hue

Question 5

Key components to any good story

Answer 5

Setup, conflict, resolution

Question 6

Dashboards

Answer 6

Tools that enable users to monitor metrics and explore data interactively

Question 7

Exploratory vs. Explanatory visualisations

Answer 7

Exploratory visualisations are for us to explore data and find patterns
Explanatory visualisations are for others to communicate and explain the patterns we have found through exploring the data

Question 8

Best visualisations to capture a trend over time

Answer 8

Line chart: to capture how a numeric value changes over time
Multi-line chart: to capture how multiple numeric values change over time
Area chart: shows how a numeric value progresses by shading the area between the axis
Stacked area chart: to track the breakdown of a numeric value by subgroups
Spline chart: line chart in which data points are connected with smoothed curves to account for missing values

Question 9

Best visualisations to visualise a single value

Answer 9

Card: show and track KPIs in dashboards or presentations
Table chart: best for small datasets, displays tabular data in a table
Gauge chart: often used in dashboard reports to show relevant KPIs

Question 10

Best visualisations to capture distributions

Answer 10

Histogram: shows the distribution of a variable. Converts numerical data into bins as columns. X-axis shows the range and y-axis the frequency
Box plot: shows the distribution of a variable using 5 key summary statistics - minimum, first quartile, median, third quartile and maximum
Violin plot: variation of the box plot
Density plot: visualise a distribution by using smoothing to better capture the distribution shape of the data

Question 11

Best visualisations to visualise relationships

Answer 11

Bar chart: one of the easiest charts to read which helps in quick comparison of categorical data. One axis contains categories and the other represents values
Column chart (vertical bar chart): categories are placed on the x-axis. Preferred over bar charts for short labels, date ranges or negatives in values
Scatter plot: most commonly used when observing relationships between two variables. Useful to spot potential correlations between data points
Connected scatter plot: hybrid between scatter plot and line chart, the scatter dots are connected with a line
Bubble chart: to visualise data points with 3 dimensions (namely visualised in the x and y axis and with the size of a bubble)
Word cloud chart: for visualising the most prevalent words in a text

Question 12

Best visualisations to visualise a flow

Answer 12

Sankey chart: for representing flows in systems of any measurable quantity
Chord chart: for presenting weighted relationships or flows between nodes and highlighting the dominant or important flows
Network chart: consists of nodes and interconnected edges and illustrates how different items have relationships with each other

Question 13

Best visualisations to visualise part-to-whole data

Answer 13

Pie chart: one of the most common, commonly used with percentages (useful with a small number of categories)
Donut pie chart: variant of the pie chart with a hole in the centre for readability
Heat maps: two-dimensional charts that use colour shading to represent data trends
Stacked column chart: best to compare subcategories within categorical data and to compare percentages
Treemap charts: 2D rectangles whose size is proportional to the value being measured and used to display hierarchically structured data

Question 14

Line plot

Answer 14

Show changes in numeric values over time

Question 15

Bar plot

Answer 15

Visualises numeric values by categories (ranked or unranked)

Question 16

Histogram

Answer 16

Shows the distribution of numeric values

Question 17

Scatter plot

Answer 17

Shows the relationship between two numeric values
Specially useful to find potential correlations between data points

Question 18

Box plot

Answer 18

• Summarises the distribution of a dataset by showing its central tendency, spread, and any outliers
• The box represents the middle 50% of the data and the “whiskers” extend to the minimum and maximum values within a certain range. Outliers are plotted separately