In this and the next two videos,
I'll continue my introduction to data science
by presenting more on simulation and introducing
static visualizations.
Now, in this particular series of three videos,
we're going to be demonstrating static bar charts
that show the final results of a simulation
in which we roll a six-sided die
a specified number of times, and in the next video,
I'll demonstrate the script that we're going to
produce in action, and then in the subsequent video,
we'll take a look at the actual source code
for that simulation.
Now, in general when you are doing data science,
visualizations are extremely important
and can be a great way for you to get to know your data.
So, very quickly, with these powerful
visualization libraries that are available to us
with Python, we can hand off a chunk of data
to a library and instantly get these
really nice charts and graphs that can help us
get a sense of the data.
Now, let's say we're rolling
a six-sided die six million times.
By summarizing how many times each die face occurs
and then plotting that on a bar chart,
we can get a sense of whether or not
we get approximately the same number of
ones, twos, threes, fours, fives, and sixes,
which is the key thing that we'll be showing
in this particular example.
So, that's just one example, but as you get into big data,
sometimes the data is so massive
that the only ways to really get to know the data
are to calculate things like descriptive statistics
that we've started to present to you
or to take a look at graphs that summarize the data for you.
Now, we'll be looking, in this sequence of three videos,
at the Seaborn and Matplotlib libraries.
Matplotlib is a very popular visualization library.
It happens to also have support built
into Jupiter Notebooks, which makes it
even more popular for that reason.
And then, separately, Seaborn is built
on top of Matplotlib, and then also, therefore,
can work in context of Jupiter Notebooks.
And Seaborn, basically, is going to simplify
a number of Matplotlib operations.
It also beautifies a few things about Matplotlib as well.
So in the next video, we'll take a look at
the running simulation and talk about
what we're trying to show, and then
in the subsequent video, we will demonstrate
the code for that simulation.