Let us begin with the fundamentals of visualization.
What you see on the right side is a picture graph or
a bar chart prepared by students in kindergarten class,
four-year-olds at the St.
Luke Catholic school in Mississauga.
This bar chart presents
the frequency for transportation modes
the kids have used to come to school.
Some have been dropped to school by car,
others by bus, and two students walked to school.
In the brave, big world of big data,
we can see that children are being trained at
the earliest possible age with data and data science.
The most important thing to realize is
that the type of visualization
you will use depends upon the type of
variables you're trying to analyze.
For instance, if you're working with
categorical variables such as gender,
you have to rely on
a certain type of charting tools than if you
were to be working with continuous variables
such as age and income.
In one case, you may be using bar charts,
in another case, you will be
required to use scatter plots.
I would like to draw your attention to
the Extreme Presentation Method
developed by Dr. Andrew Abela.
Essentially, it lays out the possible ways of depicting
data based on what kind of
variables you have at your disposal.
This visualization, this graphic
is developed by Dr. Abela that
shows that if you are interested in
comparing variables or demonstrating
their distribution or composition or
the relationship between two variables or more,
you have to rely on specific type of graph.
If you're comparing items with few categories,
you can use bar charts or column charts.
If you are comparing behaviors over time and if you
have the time periods running into several months,
you may have to use a line chart.
If the time periods are not that many,
then you can use columns and other approaches.
If you're trying to depict relationships
between two continuous variables,
your choice is a scatterplot.
The bubble chart will depict two variables on x and
y axis and the third variable
will be depicted by the size of the circle.
So you can essentially have three variables.
If you're interested in depicting
the distribution of the dataset,
you can use the histogram,
which could be a bar chart type of
histogram or a line histogram.
The distribution could also be
shown through scatter plots.
If you would like to show the composition,
then if it's a static data,
you can use pie charts.
If you're showing the composition that
changes over time for few periods,
you can use stacked columns.
If you have several periods,
you can use the stacked area charts.
For hands-on in Python,
we will be using the Seaborn library and
the Matplotlib library to
create visualizations in the labs.
We will learn how to use different functions within
the library to create different kinds of charts.