4 Types of Data: Nominal, Ordinal, Discrete, and Continuous

Data consists of several verifiable facts that are used in the analytical process to make up the data. Data today is used across each and every industry. As a result, most businesses base their strategy decisions on data insights. It is employed to launch new products and services or test out novel concepts. According to various surveys conducted, at least 2.5 quintillion bytes of data are produced daily today. One of the key concepts in Data Science is the use of different types of data. 

Simply put, data is a systematic collection of digital facts and numbers that have been acquired from digital exchanges. Different statistical data types provide insight for making future predictions and enhancing current services. Millions of organisations have benefited from the constant influx of data by making fact-based decisions that have led to growth. Data types are the numerous categories into which a great amount of information has been divided in order to obtain different types, qualities, and features of data.

Data is classified into 4 basic categories namely,

• Nominal Data
• Ordinal Data
• Discrete Data
• Continuous Data

Qualitative or Categorical Data

Data that cannot be quantified or tallied in numerical form is referred to as qualitative or categorical data. These kinds of data are organised by category rather than by quantity. It also goes by the name Categorical Data for this reason. These data can be text, symbols, audio, or images. A person's gender, whether it be male, female, or another, is qualitative information.

Qualitative data describes how people see things. Market researchers can use this information to better understand the preferences of their target market and then adapt their ideas and approaches. 

Some of the examples of Qualitative Data are listed below,

• Opinion on something (agree, disagree or neutral)
• Open-ended Surveys
• Case Studies

Qualitative Data is further classified into 2 broad categories, Nominal Data and Ordinal Data. 

Nominal Data

Variables with no order or numerical value are labelled using nominal data. 

The word "Nominal" is derived from the Latin word "nomen," which means "name." Nominal data prevents us from performing any mathematical operations or from arranging the data in any particular way. These data are split into different categories but lack any meaningful order. 

Examples of Nominal Data are listed below,

• Nationality
• Gender
• Eye Color
• Marital Status

Ordinal Data

Ordinal data have a natural ordering in which the numbers are arranged in some way according to their scale positions. These statistics are used to track things like consumer pleasure and satisfaction, but we can't do any mathematical operations on them. Ordinal data are qualitative data that have some sort of relative location for their values. These types of information might be viewed as "in-between" qualitative and quantitative information. Ordinal data cannot be used for statistical analysis because it only displays sequences. Ordinal data differ from nominal data in that they exhibit some sort of order that is missing from nominal data.

Some of the examples of Ordinal Data are listed below,

• Letter grades in exams (A, B, C, D, E & F)
• Ranking order of people in a competition
• Financial status (High, medium & low)
• Education level (Higher, Secondary & Primary)

Difference between Nominal and Ordinal Data

Quantitative Data

Since numerical values can be used to express quantitative data, they can be counted and include statistical data analysis. These data also go by the name of numerical data. It provides a response to the questions such as, "how much," "how many," and "how often." Quantitative data includes things like a phone's cost, a computer's RAM, a person's height or weight, etc. Statistical manipulation can be done with quantitative data. A wide range of graphs and charts, including bar graphs, histograms, scatter plots, boxplots, pie charts, and line graphs, among others, can be used to display these data.

Some of the examples of Quantitative Data are as follows,

• Height or Weight of a person
• Room temperature
• Match scores

Quantitative data is further divided into 2 broad categories namely, Discrete Data and Continuous Data. 

Discrete Data

Discrete refers to anything unique or separate. The values that fall under integers or whole numbers exist in the discrete data. Discrete data includes things like the overall number of students in the class. There is no way to convert these data into decimal or fractional values. The discrete data cannot be subdivided because they are countable and have finite values. Typically, a bar graph, number line, or frequency table is used to depict these data.

Examples of discrete data include,

• The total number of students present in a class
• The number of employees in an organization
• Days in a week

Continuous Data

Fractional numbers are the representation of continuous data. It may be an Android phone's version, someone's height, the size of an object, etc. Information that can be broken down into lesser levels is represented by continuous data. Any value within a range can be assigned to the continuous variable. The main distinction between discrete and continuous data is the presence of the integer or whole number in discrete data. The fractional values are still stored in continuous data to record various forms of data, including temperature, height, width, time, speed, etc.

Examples of Continuous Data are as follows,

• Height and weight of a person
• Speed of a vehicle
• Market share price

Difference between Discrete and Continuous Data

Conclusion

The article covers the various data types and their distinctions. Working with data is essential because one must determine the kind of data it is and how to use it to get beneficial outcomes. Knowing which type of plot works best for a certain category of data is also crucial because it facilitates data analysis and visualisation. Data science skills and a thorough knowledge of the various forms of data and how to interact with them are essential for working with data. 

Research, analysis, statistical analysis, data visualisation, and data science all employ many sorts of data. This information aids a business in business analysis, strategy development, and the development of an effective data-driven decision-making process.