Whether you are a data professional or a businessman, almost all who are related to marketing and the business field requires to deal with different kind of data. Understanding the data types and their variety helps the marketers, business men, data analyst, data scientists, and many more in picking up the right types in order to make correct and most accurate future trend prediction. Also, when the data professional know different kind of data, it becomes easy for them to choose the right type that can meet their requirement and goals very closely.
With the proper understanding of the data type, the data professional will get the proper idea which data type to select in order to make correct measurement and thus the correct prediction as well as decision.
Here in this guide, we are listing you different kinds of data on the basis of data science, research, and statistics thus helping all those who have to deal with data in having better understanding of its types.
In general data on the basis of statistics, data science, and research, it is of two different categories and total four subcategories. These are:
Qualitative data: That expresses the quality of the object like color name, thick, thin, etc.
- Nominal data: Example Gender, color, etc.
- Ordinal data: Example Grade A,B, first grade, second grade, etc.
Quantitative data: It expresses as the number or measurement of the object.
- Discrete data: Like number of workers in office, number of students in class.
- Continuous data: Like height of the children, measurement of a room, etc.
Types of data
Following are the description of different categories of data:
This kind of data is most commonly used in daily life as well. It is expressed in the terms of number or any other kind of measurement. This kind of data is helpful in answering the questions like how much, how many, and how often.
The person can measure the quantitative type data and is generally expressed in numerical variables. In simple language, we can say that quantitative data is that kind of data which can be measured in numbers.
Data professionals and others can easily represent the quantitative kind of data in variety of statistical charts and graphs like bar graph, line, scatter plot, and many more.
Examples of quantitative data includes that that can be expressed in number like test scores. The exam scores are generally in numbers like 80%, 85%, 90%, etc. Other examples of quantitative data are like the weight of the person, temperature of the room, shoe size, etc.
As mentioned before, quantitative data is further divided into two sub categories that are discrete and continuous type data.
The qualitative data type is a data type which is generally cannot be measured but can represent the quality of the object. They are generally written in words, symbols, pictures, but can not be measured in numbers. The other name for the qualitative data is categorical data and it is helpful in providing the information related to the object. They can sort the data by their quality not number. For example, the gender can sort male and female in two categories. In this example, gender is a qualitative data which helps in sorting the males and females in two categories by their gender quality not number.
The example of qualitative data are the colors like colors of fruits. The name of your favorite vacation destination is also a qualitative data. Other examples of qualitative data are name of the persona, ethnicity, etc.
The nominal data is a subcategory of the qualitative data which is useful in labeling the variable without any kind of use of measurement or the quantifiable value. The term nominal data comes from the Latin word which refers to the name of the object.
With the help of nominal data, it is possible only to name the object but it cannot sort the objects in any order. This kind of data is generally used in labeling the object.
Example for the nominal data includes the gender, marital status, color, ethnicity, etc. As you can understand from the examples, the nominal data can only sort the same kind of data or label the data but cannot order them from highest value to lowest value. The gender can be either male or female but it is just a label and cannot order the data.
Ordinal data is classified under qualitative data but is actually somewhere between the qualitative and quantitative data. This data though cannot measure the data but can order them from highest level to lowest level. This kind of data is generally used in placing the object in some type of order as per the position or the scale of the object. It is generally useful in representing the superiority of the object.
Though this data type is helpful in ordering the object but cannot measure them arithmetically. Example of the ordinal data includes grades like A, B, c, etc. Another example of ordinal data is ratings of the employees on the scale from 1 to 10.
This kind of data is represented in integer data and it cannot be in fraction. This data type is useful in measuring the objects in arithmetical numbers like 1, 2, 3 etc. The discrete data cannot be in fraction and they are always the whole number.
Example of discrete data are the number of children in class. The number of children is always in whole number, 1.5 or fraction number students is not possible.
This data type is also a quantifiable data that can be divided in finer levels. They are generally measured on scale and it can be any of the numerical value. Example of the continues data is the height of the person. It can be any value and even in fractions.
The bottom line
So these are different kinds of data that all of data professional should be knowledgeable of. With the proper information and understanding of the above mentioned data, the user can more accurately predict the result.