Understanding the Types of Data

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By Vijay Singh Khatri

Data science is all about how you can find out new ways to make your data more structured and useful from its raw form. Data in the age of the Internet is the fuel that drives profit to a business and leads it to the right path.

With the correct data, you will be able to have actionable insights about what you need to do next that will help you in strategizing current campaigns. Everything that we have mentioned above has one thing in common, and that is the data. We are entering the world of digital connectivity, where every device can communicate with the other. For example, a company like Amazon is collecting more than 5TBs of data each day from its users. This clearly shows data is the gold of the modern age. So it becomes necessary to know what are the different forms of data and how you can use them according to your benefit.

Today, in this article, we are going to provide you with a list of different types of data and where you use them in your day-to-day work. Besides, we will also show you how data is important and what benefits you can get from it for your business or company. So let’s begin.

Importance of Understanding Data Types

As we said earlier, data is the new gold; right now, as you are reading this article, you are also consuming some form of data and giving your Internet provider your data as well. The word data came from the Latin word “Datum”, which basically means giving something. The data in the modern age has become so important that without taking care of it, a business or a company could lose tons of important insights into what their customers are thinking about the brand and what customers want from your business.

Qualitative Data Type

A qualitative data type is the one that gives users the object under consideration that uses a finite set of discrete classes. What this means is you cannot count this data or measure it using the number system to divide them into different categories. The gender of individuals in a country can be termed as a good example of a qualitative data type.

Apart from this, the qualitative data types are the ones that are extracted from your audio, images, and even text medium. Another example of where qualitative data type is used is in the information provided by a smartphone manufacturer when they release a new mobile in the market. The smartphone company will tell you about the different colors in which the smartphone will come, the different types of memory storage you can go with, and more. All of this information can be summed up as a qualitative data type.

The Qualitative Data type can further be categorized into Nominal and Ordinal.


These are the qualitative data types that do not follow any form of the natural order. Let us take an example to make things more clear. The different color options you can choose from when you are thinking about painting a wall in your home. Here we cannot compare one color with the other; that is, it is not possible for us to say red is better than pink as some people might like red over pink, while others prefer pink over red.

The same goes for the gender of a person, and there is no greater, equal, or lesser when it comes to genders. A person could be male, female, or others. On the other hand, if you look at the categories of a mobile phone, you can see these two are not determined specifically as a mid-range smartphone for some could be the affordable one. At the same time, the high-end smartphone could be a mid-range smartphone for some people. In these types of cases, the data present is called nominal.


The ordinal data types have a natural order to them while still being in the class of their values. Let’s take an example here; think about the size of a clothing brand, then we can quickly sort them according to their name tag, such as small, medium, large, and extra-large. Besides, the ordinal data type can be considered as one where we have a grading system. So when you get an A+ in an exam, it is always better than a B- grade.

With these categories, we can easily come up with the solution to choose which encoding strategy can be applied to the data type. The data encoding for these two subcategories of qualitative data type is important because machine learning models are not designed to handle these values on their own directly. These values need to be converted into numeric types, so the models are mathematical.

For the nominal types where we cannot get any form of comparison to convert them into mathematical types, we have to rely on one-hot encoding, which is pretty similar to binary coding considering you will have to deal with fewer numbers. On the other hand, for the ordinal type, label coding is more than enough to get the job done. Some people prefer the term ‘Integer Coding’ rather than ‘Label Coding’, so don’t get yourself confused.

Quantitative Data Type

This form of data type is the one that tries to quantify things, and it does it by using numerical values, which allows it to make them countable. For example, look at the price of smartphones, the discounts offered on various products online, the number of ratings on products, the frequency of processors present in your computer, RAM that is installed on your smartphone or computer, and more. All of these things tend to fall under the category of quantitative data type.

The thing that you need to know about quantitative data type is that it can be an infinite number of values. For example, let’s look at the prices of smartphones. They start from X amount and go up to Y amount depending on the hardware used in them. But still, some companies use the customization of smartphones with jewels to take them to the next level, and thus, the price increases depending on the embedded jewels. The numbers in quantitative data type can be again broken down into various fractional values. The two subcategories that describe them clearly are as below:


The discrete values are the ones that come under the category of integers, or you can place them as whole numbers. For example, the number of speakers present in a theater, cameras present in your smartphone, cores present in your computer, and more. All of these are examples of discrete data types.


The fractional numbers are considered to be continuous values. They can also be used as an operating frequency for the radios, processors, Android versions, and more. The important aspect of continuous data is that it can be divided into much smaller levels, and the continuous variable that you are using can take any value present within a range.

The main difference between the discrete and the continuous data is that the discrete data contains the integer or whole number. Even after that, continuous data can store the fractional numbers to record the data types like temperature, the height of an individual, time, speed, and more.

Can We Overlap Discrete & Ordinal Data Types?

Even if you give numbers to the ordinal classes, they are not converted into discrete. That’s because even after the numbering is done for ordinary classes, it still doesn’t clarify what the actual distance is between the classes. For example, if we use the grading system of a test that has A, B, C, D, and E, and we associate them with numbers 1,2,3,4, and 5. Now when we look at them, the numerical difference between grades E and D is the same as the distance between A and B. But that’s not true because a D grade still means you are passing the subject, but an E means you are failing in the subject. With the use of numbering, we are removing the difference between them and declaring A, B, C, D, and E to be equal.


So this was all about the different types of data that we use in our daily lives. The knowledge of which one to choose at the appropriate time can help us in saving a lot of time and cost that we spend on collecting and then finding the insights in the data.

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