The most basic building block of data in data
science is a scalar data type.
Scalar data types store a single unit of data.
This can be a letter, a number, a date, a
time, or something else.
We refer to them as scalar data types because
a scalar variable in mathematics can hold
one and only one value at a time.
Scalar data types are also the most basic
unit of storage for data in a computer.
Everything from a small text document to a
giant distributed database are composed of
these single units of storage.
Scalar data types provide a set of operations
that can be performed on the data they contain.
All of the processing that occurs in a computer
is essentially the result of these operations
being executed on scalar data types.
There are several scalar data types commonly
used across various computers, programming
languages, and data-science tools.
The most common data types you will encounter
in data science can be divided into three
main groups: – categorical data types, – numerical
data types, – and Temporal data.
Let’s take a look at a few of the most common
scalar data types we encounter in data science.