Data science : Data types - Foundation

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I have just started Data science path and this is my first blog post related to data science.

It is important to understand the data types, before we get deep in to the data science.

Why we need to understand the data types, the reason is statistical analysis depends on the type of data.

Data types

Qualitative: 

This has two types of data types - Normal & Ordinal

Normal : This data type represents the labels. 
                Examples : Gender - Male/Female, Color: Red/Blue/Block

Ordinal : It is similar to "Normal" data type, except that it contains the natural ordering. Ordinal values can also be expressed in numbers.

                Example: Rating: Very Poor, Poor, Average, Good, Very Good.


The only difference between Normal/Ordinal is that, there is no ordering in Normal data type where as in Ordinal data type has ordering in it.

Quantitative:

This has two types of data types - Discrete & Continuous.

Discrete: This data type represents the integer values with decimal point.
                Examples : No of students in the class, Marks in a subject. Number of runs made in a cricket                 match.

Continuous: This data type represents the numbers with decimal point.
                Example : Salary, Height/weight of a person.


We can ask the questions on data based on the above data types:
  • If the data type is Qualitative, we can't ask average of the color in the list of shirts, but we can ask the count shirts that has color red.
  • If the data type is Quantitative, 

Examples of Qualitative & Quantitative in different domains.
Agriculture:
  • Qualitative:  Type of land, type of seed, type of fertilizer.
  • Quantitative: Acres of land, spend of the paddy, returns on paddy. No of formers
Banking:
  • Qualitative:  Type of account(Saving/Current), Gender/Home branch/ of the account holder 
  • Quantitative: Account balance, No of loans, Transaction date.


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