- Nominal
- Ordinal
- Discrete
- Continuous.
Types of Data
1. What is Qualitative or Categorical Data?
Qualitative or Categorical Data is data that can’t be measured or counted in the form of numbers. These types of data are sorted by category, not by number. That’s why it is also known as Categorical Data. These data consist of audio, images, symbols, or text. The gender of a person, i.e., male, female, or others, is qualitative data.
Qualitative data tells about the perception of people. This
data helps market researchers understand the customers’ tastes and then design
their ideas and strategies accordingly.
The Qualitative data are further classified into two parts:
1.1 What is Nominal Data?
Nominal Data is used to label variables without any order or quantitative value. The color of hair can be considered nominal data, as one color can’t be compared with another color.
The name “nominal” comes from the Latin name “nomen,” which means “name.” With the help of nominal data, we can’t do any numerical tasks or can’t give any order to sort the data. These data don’t have any meaningful order; their values are distributed into distinct categories.
Examples of Nominal Data:
- Colour of hair (Blonde, red, Brown, Black, etc.)
- Marital status (Single, Widowed, Married)
- Nationality (Indian, German, American)
- Gender (Male, Female, Others)
- Eye Colour (Black, Brown, etc.)
1.2 What is Ordinal Data?
Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observation like customer satisfaction, happiness, etc., but we can’t do any arithmetical tasks on them.
The ordinal data is qualitative data for which their values
have some kind of relative position. These kinds of data can be considered as
“in-between” qualitative data and quantitative data. The ordinal data only
shows the sequences and cannot use for statistical analysis. Compared to the
nominal data, ordinal data have some kind of order that is not present in
nominal data.
Examples of Ordinal Data:
- When companies ask for feedback, experience, or satisfaction on a scale of 1 to 10
- Letter grades in the exam (A, B, C, D, etc.)
- Ranking of people in a competition (First, Second, Third, etc.)
- Economic Status (High, Medium, and Low)
- Education Level (Higher, Secondary, Primary)
2. What is Quantitative Data?
Quantitative data can be expressed in numerical values, which makes it countable and includes statistical data analysis. These kinds of data are also known as Numerical data. It answers the questions like, “how much,” “how many,” and “how often.” For example, the price of a phone, the computer’s ram, the height or weight of a person, etc., falls under the quantitative data.
Quantitative data can be used for statistical manipulation
and these data can be represented on a wide variety of graphs and charts such
as bar graphs, histograms, scatter plots, boxplot, pie charts, line graphs,
etc.
Examples of Quantitative Data:
- Height or weight of a person or object
- Room Temperature
- Scores and Marks (Ex: 59, 80, 60, etc.)
- Time
The Quantitative data are further classified into two parts:
2.1 What is Discrete Data
Discrete means are distinct or separate. The discrete data contain the values that fall under integers or whole numbers. The total number of students in a class is an example of discrete data. These data can’t be broken into decimal or fraction values.
The discrete data are countable and have finite values; their subdivision is not possible. These data are represented mainly by a bar graph, number line, or frequency table.
Examples of Discrete Data:
- Total numbers of students present in a class
- Cost of a cell phone
- Numbers of employees in a company
- The total number of players who participated in a competition
- Days in a week
2.2 What is Continuous Data?
Continuous data are in the form of fractional numbers. It can be the version of an android phone, the height of a person, the length of an object, etc. Continuous data represents information that can be divided into smaller levels. The continuous variable can take any value within a range.
The key difference between discrete and continuous data is that discrete data contains the integer or whole number. Still, continuous data stores the fractional numbers to record different types of data such as temperature, height, width, time, speed, etc.
Examples of Continuous Data :
- Height of a person
- Speed of a vehicle
- “Time-taken” to finish the work
- Wi-Fi Frequency
- Market share price
Different types of data are used in research, analysis, statistics, and data science. This data helps a company analyze its business, design its strategies, and help build a successful data-driven decision-making process.
