Building upon scalar data types, we also have
composite data types in data science.
A composite data type is a logical container
used to organize related data.
It contains a set of scalar data types organized
in a specific way.
Composite data types allow us to store and
access information effectively.
They provide methods for accessing individual
scalar values and performing operations on
groups of scalar values.
In addition, composite data types provide
context to related data which (as we discussed
previously) is used to create information.
You can think of a composite data type as
a container that holds a collection of related
data in a specific way.
There are several types of composite data
types that we encounter in data science.
They can be grouped together in various ways.
However, to keep things simple, we’re going
to organize them into the following four groups:
– Homogenous data types – which require all
data to be of the same data type
– Tabular data types – which store data in
– Semi-structured data types – which store
data as a set of relationships
And multi-media data types – which store
data for images, audio, video, and more.
Let’s take a look at a few of the most common
composite data types we encounter in data science.