Identifying 3 basic Flavours of data for processing.

Before stepping into data processing we have to identify the what's flavour of the data. before applying fancy algorithm or basic statistics like mean median or mode, so our results wont deviated, so basically we can divide data in three parts.

1)  Numerical.

Numerical data represented in digits which might express count of items, weight of person, height of building, package size of an item so on. we can further divide numerical data into

Discrete : This will take specific value like count of items, like Number of apples in a crate, no of brake pads in a car. this will not specify range but and definite value.

Continuous: this type of data will not specify specify value, this could be running number with is a range, like no passengers could fit in small car , height of persons with in certain age group, this might denote the range rather than definite value.

2) Categorical data

Grouped data  which is categorised by name, Any Discrete or Continuous Numerical value which represented by a finite set of Categories is called as Categorical data. like genres of movies launched in  an year. segment of cars sold in year,or country.

lets take Segment of cars like SUV, Sedan,Cross over,Hatch back might be few more but this is identified as an finite set. this can hold Discrete value like count of cars, no of bands available in the Segment, or number premium card in the segment, or it hold Continuous numerical value like the selling price range in the segment.

3) Ordinal Data

Ordinal Data is an Categorical data in natural order, whose distance between the category is not known. like Likert scale. Where you have set of questions and you have specified rating number from 1 to 5 or 1 to 100. Assume ratting system for an movie, User experience star rating system are categorised ad Ordinal data.

So these above 3 flavours further  can be grouped into Quantitative (1 & 2) And Qualitative (3)  also we have other Qualitative data such as Binary Distributed, Nominal distributed data.

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