As we have seen, performing data abstraction is
a very useful exercise to make decisions
about what are appropriate visual representations for your data.
Now before we conclude,
I want to make it clear that data abstraction is not
the only process or
the only method that you typically have to use in a data visualization project.
There is much more than that.
So, connected to data abstraction is the idea that
you have to become familiar with your data set.
As you have seen in the previous examples,
sometimes you just don't know what is the real meaning of a given attribute type.
Data can come from many different sources,
the meaning may actually not be evident at all.
And typically what happens is that either you need to do some research on your own
or if you have a client you will
need to talk with your client who is providing data to you,
to understand in a much more deeper sense,
what is the meaning of every single attribute.
So, this activity is often called data profiling.
I also like the term data familiarization
because you have to become familiar with your data.
And I want to make sure that you realize that in
practice what happens is that this activity
takes some time and also requires interacting with other people who have the knowledge,
a much deeper knowledge than you,
of what is the meaning of the attributes that are contained in your data set.
Before we conclude, I want to point out that
another very important activity that is typically
performed at the beginning of a data visualization project,
is checking data quality and doing what is called data wrangling.
So, let me talk about these two elements.
Data quality means that data often comes in
ways that are not necessarily precise or complete.
Right? For instance, many,
many real world data sets out there have missing values, right?
So, what do you do if some value is missing in one or more attributes?
You'll have to decide how to fix this problem.
Right? Or sometimes there are wrong values,
say you have a quantitative attribute and one single instance,
one single item as one value that is completely off the mark.
You'll have to detect this problem and you have to find a way to fix it.
Right? So, the activities that are necessary to identify these problems and also fix
these problems so that you can change that sum of
the values of your data or even transforming your data from one type to another,
something we will touch upon in the next lesson.
Yes, you'll need to perform these activities.
So before I conclude,
I want to point out that this course can't cover all these elements.
So, these elements are typically covered in
other courses that focus on data manipulation.
So, if you want to know more about data manipulation,
you want to learn more about data manipulation,
I encourage you to find other courses that focus on this specific problem.
In this course, we focus on the problem of the data abstraction because as I have shown,
it's very useful to you to decide what
visual representations are available and effective.
And in the next lesson,
I will give you a little information about what data transformations are useful.
Again, focusing on transformations that are useful to visualize data better.
But I think it's important for you to realize that this topic is much larger than that.
And once again, if you want to know more about it,
you'll have to find courses that address these problems specifically.
