Data cleaning and data transformation before EDA? What is the most common order of data cleaning, data transformation and exploratory data analysis?

For me it seems most logical to do data cleaning, then EDA and finally data transformation (encoding of categorical variables, and feature scaling).

Doing data transformation before the EDA, seems to make the EDA not that useful, as you cant ex. check for stuff like:


  Passengers in the age interval 0-18 has a higher chance of survival


(if feature scaling has been applied to age feature).

But then again, doing data transformation after the EDA, also miss out on chance of encoding categorical variables and thereby visualize correlations of those with the target variable.

What is the order of the mentioned processes? And is there even an order?
Although not very helpful, the answer is probably "it depends".

I like to do data cleaning and some EDA together since EDA can highlight appropriate treatments to clean the data - e.g. influencing how to handle missing values.

I think data transformation should be done just prior to modelling; whether or not you need to do any transformation at all depends on the techniques you plan to use.