In this session, let's understand the different types of data we all know the two broad categorizations
when it comes to data, qualitative data and quantitative data.
But if you really see a further subdivision is there between qualitative and quantitative data.
Let's see quantitative data.
We have continuous and discrete motor quantitative.
The key difference is continuous can be measured on a continuum or scale.
You can subdivide this meaningfully.
For example, if someone says 169 centimeters is their height, you can represent it as one point six
millimeters also.
Right.
That kind of subdividing of data is not possible and discrete.
Yeah, I'm primarily referring to count of data.
Suppose in a class I can say there are 30 students, I cannot say thirty point one students or point
three students.
Right.
Do you understand the difference between discrete and continuous?
Right now, let's see qualitative data in qualitative.
Let's start with binary binaries, two values are possible, right?
Pass fail.
Yes.
No, OK.
Apart from binary, you have nominal ordinal in qualitative data.
Ordinal is about an ordered series, nominal, the inherent ranking or order is not there.
Examples are gender, right?
Race.
You can't say that male is better than a female, right.
So there is no inherent ranking or order, whereas in the case of ordinal, that order or ranking is
possible when it comes to performance.
Grade is better than grade B, grade BS, but better than grade see, so on and so forth, right.
So you need to ascertain what are the different types of data that are there in the dataset that are
taking for analysis why this is important?
You have to understand that algorithms are developed only for quantitative data.
So the quantitative data must be converted into quantitative data.
That is why we need to isolate the qualitative data in our dataset.
So please keep this up in your mind.
OK, now let's see some real life examples.
In the case of insurance, we do this as part of deponent, an independent variable, right?
I'm taking the same example.
If you see this, the insurance charges that must be charged by the insurance company is dependent on
so many factors that you really see.
Why is numeric X1 is numeric, extra is numeric exploders.
Normally the rest are non numeric.
OK, you must convert them into numeric.
Right.
OK, the other example is the banking example in this case, if you see the wire itself is not numeric,
right?
It's a case of should loan be granted or not.
Isabell, only number of dependents, applicants, income, koplik and income loan tenure and amount
visa numeric data.
The rest are all non numeric.
OK, that is one, two, three, four, five.
Only five out of 11 factors will have numeric data.
The remaining will have no numeric.
Right.
So do you understand the different types of data that will be there in your data center?
OK.