Understanding the type of data that you are using is very important for analyzing it for whatever purpose you are using it for. The main classification of data types can be done into Quantitative and Qualitative as a very broad classification.
Quantitative data type:
This type of data deals with numbers. The data which can be measured on a scale of numbers is called quantitative data. We come across this in our daily lives. For example, we have seen different sizes of objects, weights, temperature, cost, area, etc.
This data is any measurable knowledge that can be used for quantitative equations and statistical study, such that choices based on such scientific derivations can be taken in real life.
Qualitative data type:
This type of data is of a non-numerical kind. This type of data is obtained by evaluation techniques, one-to-one interactions, focus group interviews, and related approaches. Qualitative data is also regarded as categorical data in analytics. Data which can be conclusively ordered dependent on an item or a phenomenon ‘s characteristics and properties.
Qualitative data is essential in deciding the occurrence of traits or apps. It helps the statistician or the analysts to shape criteria from which to analyze broader data sets. Qualitative evidence includes the means of quantifying the universe surrounding them through observers.
The distinction between qualitative and quantitative data is very simple to grasp, qualitative data does not have statistics in the interpretation of characteristics while quantitative data is all about figures.
Quantitative data can be further categorized into two subcategories:
Continuous data type:
Information measured on a graph or on a scale. Continuous details may have nearly any numerical significance, which can be subdivided into smaller which smaller measurements, based on the measuring system’s accuracy. For example: measuring the height of a people, everyone will have a different height which can be measured on a height scale. There are segments of height further and further divided to measure accurately.
We can see in the geometry; data may be constant or in the meaning spectrum constant. For a single data object, the set of values has a minimum and a maximum value. Continuous data may be any intermediate meaning. This is the evidence that is observable on a scale.
Discrete data type:
Only specific values can be taken from discrete data. An infinite number of such values that occur, but each one is distinctive but there is no grey area between them. Usually, it involves integer numbers in it. For example, the number of children in a class or the number of apples in a basket. These individual units cannot be divided and called as a unit. Therefore, they are a part of discrete data.
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