Today I will be covering the difference between discrete vs continuous data. This is an important concept to grasp as a data analyst and one that can confuse some data newbies.


What is discrete data?

Discrete data has the following characteristics: finite, countable and whole (non-divisible) numbers. Though discrete data is non-divisible, averages of this data may result in decimal numbers. For example, a family has 1, 2, 3… children. You cannot have half a child, however, the average number of children in all of the families in your data set may be 2.5.


Categorical data

Discrete data can also be made up of categorical data, and categorical data can be broken down into nominal or ordinal data.


Nominal Data

Nominal data is used for qualitative data that cannot be put into a logical sequence. It includes categorical names rather than numbers. For example, genres of film and TV can be described with words such as “Comedy”, “Drama”, “Action” etc. Though these are not numbers, this is still discrete data. Nominal data should be made up of mutually exclusive categories; for example, blood type is A, B, AB or O, you cannot have a bit of type A and a bit of type AB blood in your body.


Ordinal Data

In contrast, ordinal data may also use categories, however these can be logically sequenced. For example, temperature may be described as “freezing”, “cold”, “mild”, “warm”, “hot”. Or a survey response may be ordered from “strongly disagree”, “disagree”, “neither agree nor disagree”, “agree” to “strongly agree”.

It is important to note that though nominal and ordinal data is usually representative of words, however it may be encoded and represented with numbers in a dataset.



Discrete data is either whole, countable, finite numbers or categorical data. Think of real-world examples, you cannot have half a dog at the animal shelter, but the average number of dogs per year may result in a decimal number.

The dog breed is a good example of categorical data, even breeds that are mixed together result in their own breed — think of all the -oodle breeds you’ve heard of. If a dog has so many breeds in it, that it is completely unknown, then the category for the nominal data of “Dog Breed” may be “mut”.


Discrete Data examples:




Number of students in a school

Subjects offered by a school

Grades offered by a school

Population of a country

Names of states in a country

Age groups of a population

Number of animals in a shelter

Types of animals in a shelter

Date each animal arrives at the shelter

Number of bags of blood in a blood bank

Blood types

Result of rolling a die

Names of winners of a game

Game number

Longitude and latitude

Name of a street in that longitude/latitude

House/building numbers on the street


What is continuous data?

Continuous data is representative of continuous measures — meaning it can be broken up into fractions and decimal values.

Examples of continuous data include height, weight, mass, speed, temperature, length and distance. Time is also commonly measured in a continuous manner.

You can create discrete data from continuous data by grouping it and converting it into ordinal data (discussed above). For example, weight is a continuous measure, however it is often divided into certain groups for certain sports — e.g. the eight boxing weight classes ranging from flyweight to heavyweight boxers.

Line charts are more common for continuous data because a line suggests that the data continues from one point to another.


Rounding up

To see examples of how the nature of your data can influence your visualisations in Tableau, read my blog on dealing with discrete and continuous data in Tableau.

Did you know that Tableau uses blue to represent discrete data and green to represent continuous data? I have written a blog about blue vs green and dimensions vs measures in Tableau.

For now, I hope this blog has helped you to understand the difference between discrete and continuous data. Happy data analyzing!


Emma Wishart
Author: Emma Wishart