Some thoughts on the lab from Week 2 Lecture 2

Hi everyone,

I just finish the lab from Week 2 Lecture 2. It was interesting to see the two different dataset that could have the identical metrics but completely different visually.

However, this do make me wonder, if two different datasets have the identical metric evaluation, what difference does it make in terms of using those datasets? For example, if they are two datasets for customer rating vs waiting time, as an engineer or the manager, will the manager or engineer actually take the shape difference into consideration? I assume they literally will only see the average rating and see the heat map to find where the bad rating located and conclude that we need to answer phone faster/ or we need more man/AI to cope with the huge customer need.

If any thoughts on this topic, please comment below, it will really help me and potentially other people.

Thank you in advance and wish you a nice new week,
Jimmy (Jin Weng)

A dataset gives a distribution of a phenomena, 2 phenomenas cannot be the same, even the same phenomena changes in times. If your are interested in certain aspects of the phenomena then you use only certain part of the dataset but don’t forget they are all parts connected and related to each other.

Thank you for your reply, but I really want to know is there any real life examples that actually tells the importance of this visualisation? How bad is the consequence if we ignore the visualisation?