4 Types Of Data- Nominal, Ordinal, Discrete And Continuous

A data scientist uses techniques such as data mining to identify patterns and train data to generate business for their organisation. All these operations have one thing in common, everything requires data.

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What is data, and why is it so important?

Data can be considered the collection of information present in various formats, such as audio, text, images, videos, or other multimedia formats. This data can be generated from many sources. However, social media and online transactions are some of the biggest producers of data daily. According to the current study, Amazon produces approximately 2.5 quintillion bytes of data per day.

Hence, it is important to use data effectively and derive important information from it. In this article, we will learn about the four major types of Data commonly used. Stay with us till the end of the article to get all the insights.

Also read: Data Science Degree

What is Data?

Data is a collection of information which is collected from various sources. The raw and unstructured data are then processed to produce useful information and insights. Data can be present in various formats, such as multimedia, bits, bytes, texts, etc. It can also be present on paper, from which we need to extract it to generate useful information.

Nowadays, most big companies heavily rely on data. There is a big flow of data on a daily basis. In the computer world, data is present in binary form, such as 0 and 1. All types of data in the real world are first converted into machine language, which is then processed by computers.

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What Are The 4 Types Of Data

There are two major classifications of data. Check them out in detail below.

Types of data

1. Qualitative Data

Qualitative data is used to represent no numerical information. This data type is used to represent the qualities and characteristics of the given information, such as colour, gender, symbols, text, taste, etc. It cannot be presented in numerical form. These data are obtained from interviews, meetings, surveys, etc. They are also known as Categorical data. There are two main types of qualitative data: Nominal data and Ordinal data. Let us learn about them in detail.

Let us understand qualitative data with some examples given below.

What is the colour of your shirt?
Will you go to school today?
Are you happy?

These data are recorded in non-numerical form. Hence, they are known as qualitative data.

1. Nominal Data

Nominal data is a type of qualitative data that is used to represent data into labels based on different categories. They do not have any specific order or numerical significance. Let us understand it better with a few real-world examples.

Colours ( red, blue, green, orange, etc)
Fruits ( Apples, Bananas, Grapes, strawberries)
Gender (Male, Female, other)
Marital Status ( Single, married, divorced, widowed)
Blood type (A, AB, O, B)
Days of the week (Monday, Tuesday, Wednesday, Thursday, friday, Saturday, Sunday)

2. Ordinal Data

This is also a type of qualitative data where only non-numerical data is considered. It is almost similar to nominal data. However, there is just one major difference, ordinal data are arranged in a meaningful order, unlike nominal data, which does not follow any specific order.

Let us understand ordinal data with some examples.

Reviews ( excellent, good, fair, poor)
Educational Qualification (high school, undergraduate, postgraduate)
Grades in exam ( A, B, C, D)
Economic background ( below poverty, middle class, rich)

These are some of the most common examples of ordinal data. It follows a specific order.

Also check: What Is the Syllabus of Data Science?

Nominal Data vs Ordinal Data

Let us understand the difference between nominal and ordinal data in the table given below.

Types of Data: Nominal Data vs Ordinal data
Nominal Data	Ordinal Data
Nominal data does not follow any ordering.	Ordinal data follows a specific sequential ordering.
It cannot be compared on a scale.	It can be compared on a scale.
It is a type of qualitative or categorical data.	It is generally considered to be between qualitative and quantitative data types.
They do not present any numerical form, and we cannot perform any arithmetic operations on them.	They provide a general ordering based on which we can perform some arithmetic operations.
These data types are not used in comparison.	These data types are also used in comparison.
Examples: Gender, colour, marital status, etc.	Example: grades, reviews, educational qualifications, etc.

2. Quantitative Data

Quantitative data is a type of data that represents numerical information that we can count and measure. They are also known as Numerical data. It generally gives answers to “how many”, “how much”, etc. This data can be represented in graphical and chart forms such as bar graphs, histograms, pie charts, etc. Let us understand quantitative data with some examples.

Marks in a test
Temperature
Weight
Sales figure

These are some common examples of numerical data. It will always represent information in numerical form. There are two major types of quantitative data: Discrete and continuous. Let us know about them in detail.

1. Discrete Data

Discrete data is used to represent distinct or separate numerical values. They are discrete because they can be presented in the form of whole numbers or integers, which cannot be divided into smaller parts.

However, the discrete data can be counted and is not infinite. They can be easily represented by various graphs and charts, such as bar graphs, number lines, etc. Let us understand with a few examples given below.

Total number of students in college
Number of cars in parking area
Number of members in a family
Number of wheels in a car

2. Continuous Data

Continuous data is a data type that deals with an infinite range of numerical data. They are generally defined within a specific range, with any value within that range. It can be easily divided into smaller fractional or decimal values. They are generally used in fractional form, unlike discrete, which uses only whole numbers or integers.

The main difference between continuous data and discrete data is that discrete data cannot be presented in decimal or fractional form, while continuous data can be presented in fractional form. Let us understand it with some common examples.

Height of a person
Temperature in celsius or fahrenheit
Weight in pounds or kilograms
Distance in meter or kilometers
Share price of market

The examples given above can easily be presented in decimal or fraction form, hence known as discrete data.

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Discrete Data vs Continuous Data

Let us understand the major difference between discrete and continuous data through the table given below.

Types of Data: Discrete Data vs Continuous Data
Discrete Data	Continuous Data
Discrete data are finite and countable.	Continuous data are measurable and cannot be counted.
Discrete data consists of integers and whole numbers	Continuous data consists of fractional values.
Any value cannot be taken between a specific range	Any value can be taken within a specific range.
They are generally represented by bar graphs.	They can be represented by histograms, line graphs, etc.
It is generally represented using probability density functions.	It is represented using probability density functions.
Example: Number of students, number of children in a family, number of cars in a parking lot, etc.	Example: Height of a person, temperature, weight of a person or object, time, etc.

Types of Data FAQs

Why is data important in data science?

Data is a collection of information which is collected from various sources. The raw and unstructured data are then processed to produce useful information and insights.

How many types of data are in data science?

There are major four types of data in data science:

Nominal data
Ordinal data
Discrete data
Continuous data

What is a discrete data type?

What is Ordinal data?

Ordinal data is a type of qualitative data where only non-numerical data is considered. It is almost similar to nominal data. However, there is just one major difference, ordinal data are arranged in a meaningful order, unlike nominal data, which does not follow any specific order.