00:01
Once again, welcome to a new problem.
00:05
When you think about data, data can be numerical and data can also be categorical.
00:15
And whether or not data is numerical or categorical, data can also be structured.
00:25
So we could have structured data and we could also have unstructured data and we could also have unstructured data.
00:31
Data so when you see structured data this type of data has clear definitions so we have clear definitions of data with with patterns making searching of data making such such for data manageable such such for data manageable such data manageable and then when it comes to unstructured data all the other types of data all the other types of data are unstructured so all the types of data that you're going to do with are always going to be structured and unstructured data can be can include things like text files it can also include pdf documents on top of that when you look at structured data.
01:52
You could also see things like video and audio files and structured data on the other hand is more quantitative and objective meaning it's easy to kind of measure and express this type of data so you have two different options when it comes to both structured and structured data so coming back to a new problem coming back to a new problem so we have a real world example we have a real world example this involves application, application, situational, or business with the benefit, with the potential, potential benefits of data analytics.
03:22
So there's potential benefits of data analytics.
03:26
So you want to have a discussion, discussion on types.
03:35
Of data available in the example and this has nothing to do with not always not always data sets and some of this data data some of this data is structured and some is instructed so we have both structured and unstructured types of data you can specify things like the source of the data collection methodology examples of this type of data and how data analytics analytics data analytics questions benefit users.
04:58
So that's what you're saying in the first part of the problem.
05:03
And the second part of the problem, you want to discuss whether or not, whether or not the ultimate gallatinous papas, galatine's papas of data analytics is understanding causal relationships among variables.
05:51
So we're just going to jump into the problem.
05:55
And in this case of saying that we're saying that we want to be want to think about an example of real world example of data and let it so assume assume the use of credit cards that also that also that also involves input of financial histories financial histories through credit cards...