Hey it's Andre here again with another quick note.
Now if you want you can pause the video and read this little comic by X Casey.
But I want to talk quickly about data visualizations.
Now we talked about this idea of exploring our data right and we can explore our data as by looking
at something like mean or variance or any statistical method to explore our data.
But data visualization is another method we can use.
Along with statistics to explore our data so we often create these data visualizations like graphs to
derive meaning from data.
But it's easy to make graphs that are meaningless or misleading.
And your job as a data scientist is not just to make pretty graphs and pretty visualizations but to
make visualizations that are actually meaningful and that are accurate.
For example access is important.
You need to know what the x axis and y axis is here.
Otherwise well this graph is completely meaningless because we have no idea what it's measuring.
This is a common mistake with beginners.
That is we assume that a graph is always telling the truth.
And we jump to conclusions right away from data.
Instead we want to pause and reflect and make sure that whatever we're visualizing is accurate and testing
it with different methods trying different visualizations.
We don't want to jump to conclusions right away from data.
It takes a lot to understand this data.
So just a quick note that is just because we have data doesn't mean we have knowledge.
All right.
Back to work.