A bubble chart is a set of dots plotted between axes representing two variables. A third variable represents the size of the bubble.
A bubble chart is one of the most popular charts among researchers and analysts. It has a lot of flexibility in terms of variable binding. With some expansions, it can represent up to seven variables at once. At the same time, if not carefully designed, reading bubble charts can be challenging. That’s why it’s better not to overload the viewer's attention by plotting too many variables.
A bubble chart is most commonly used to find correlations. Clusters, as well as outliers, are also easy to spot using bubble charts.
It is a chart with one of the best data/space ratios.
A bubble chart is also known for its versatility. It gives a lot of inspiration to infographic designers and data visualization specialists. It can be turned into almost any chart: heatmap, dot plot, icon chart, tilemap or some hybrid chart.
A scatter plot is usually the first one for data exploration. Simple one-sized data marks give a clear view of every observation’s positioning in a two-variable plane. A scatter plot is often used to show relationships between numeric variables and identify patterns.
A categorical scatter plot differs from a regular scatter plot by the presence of a categorical axis. It can be just one categorical axis or both of them. It's very similar to a dot plot. Except, in this chart, the data is mostly provided in a flat table.
This chart is very similar to a scatter plot but it’s divided into four equal parts in a 2x2 matrix. It is useful if we want to group data marks for some specific type of analysis (SWOT analysis being one of the best and most well-known examples).
If you use a combination of numerical and categorical axes and the plots on the numerical axis are dense, you might run into overlapping data marks. To avoid that you can use the Jitter property along the categorical axis. It allows you to spread data marks near the category line and make the data marks more visible.