
DATA
Everything that you need to know!
Data is nothing but a Piece of Information. If I want to know the average sales of a firm for the last 6 months, I need to collect some data, and by looking at those data I can spot the trend of average sales.
Classification:
There are mainly two types of data:
- Quantitative: When data is expressed in numerical terms, eg: Height, Weight. Quantitative data is also called “Variable”, as the value can change.
- Qualitative: When the data cannot be expressed in numerical terms, example: Religion, caste. Qualitative data is also called “Attribute”.

Discrete Data: Suppose, if I ask you” How many members are there in your family?” Your answer will be 2 or 3 or 5 or 10 etc. These are all isolated values. This type of data is known as “Discrete Data”.
Continuous Data: Now if I want to know about your height, you might answer me in the following wayà5.8888 feet or 6.2222feet. So your height can take any value within a specific range i.e. between 5 & 6 or 6 & 7. This is an example of “Continuous Data”.
Discrete and Continuous both comes under the category of “Variable”.
Nominal Data There are some qualities or Attributes which cannot be compared or cannot be rank ordered. For example color of eyes, color of hair, religion cannot be ranked. As an individual, you might have your own preference but logically you cannot say that which religion is the best. We call this kind of Quality as “Nominal”.
Ordinal Data: When the quality can be ranked. Eg. If you are asked to give a feedback of this post by giving stars where 1star means “Very Bad”, 2 stars means “Bad”, 3 stars means “Good”, 4 means “Very Good” and 5 means “Excellent”. So here you are ranking the quality of this post. This is what we call Ordinal Data.
Now, if you have worked on R, you might know that R considers some data as “Factor”. The factor is nothing but a “Categorical Data”.
Categorical Data: The categorical variable represents the types of data which may be divided into groups. Those groups are finite numbers. Gender is an example of a Categorical Variable. If we divide Gender into groups we will get mainly 3 categories: “Male”, “Female” and “Others”. If R is considering any variable as a factor, and you check the structure of that variable, you can see different levels of it. If you want to convert any variable into a factor you can use as. factor () syntax.
For further reading click here.
Hope you find this useful. We will be back very soon with other interesting topics.
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