Well done [your name here]: that data was really messy and you did an excellent job getting it ready for the EDA. Just send it to Francis and take his side: he will show you how those kinds of things are performed. We do not have too much time to invest in your education now, but siding him will be useful for you anyway.

Once the boss is done complimenting you, you can send the clean data to Francis and reach his desk.

Hi there, so you did the job with this dirty data. Well done! We are now going to perform some Exploratory Data Analysis, to get a closer look at our data, and to understand where this profit drop came from.

So, that is what EDA stands for: Exploratory Data Analysis. But, let Francis introduce you into this new world: EDA is a powerful tool in our hands. In the beginning of data analysis, analysts did not give a lot of attention to it since they tended towards the direct application of modeling techniques to their data, just drawing some preliminary descriptive statistics from their data before modeling it.

But, if I find my notes about the Anscombe quartet, I will show you that those kinds of statistics cannot always be considered as reliable.

Time is short, so let's start munging our data, with some summary EDA.

OK then, it seems Francis is willing to show you how to perform EDA on your data; I will let him be your teacher for this chapter, we will meet again in the summary paragraph. Best of luck with your analyses!