-: All right, this is the last thing we're gonna talk about
during this prep week, week zero, all about data analytics.
And now we're gonna talk about, as it says here,
data analytics with a side visualization.
Because here's the thing, if you are doing data analytics
you need to visualize your data.
You cannot understand your data fully, oftentimes,
without actually seeing it with your eyeballs.
You will see stuff with your eyeballs
that you would otherwise miss.
The numbers won't tell you everything on their own,
and here is the best demonstration of it.
You can see four data sets here, okay?
Numerals I, II, III, IV.
Each dataset has x and y, two variables.
Each dataset has 11 data points.
There are 88 numbers here. This is not big data.
This is tiny, tiny little data, right?
And yet, I challenge you to analyze this data.
Tell me what you see here.
Tell me what insights you can gain.
Now, if you spent a little bit of time with this,
I bet you could tell me, eventually,
that data set IV all of the x-values
are the same number except for one.
Maybe you noticed that.
If you keep staring at this,
maybe you'll notice that all of the other x-values
for all three of the other data sets are the same,
10.0, 8.0, 13.0, 9.0, et cetera.
Maybe some of you will notice
that all of the x-values are integers .0, .0, .0,
and all the y's are dot something else.
Two decimal places, okay.
What else can we learn about this data?
I guess none of the values are below maybe 4-ish
and none of them are above 18, 19.
Like that's literally all I can tell.
So I can make absolutely no useful sense
of this data when looking at a table of numbers,
we suck at that, so that's good to know.
Now what do we do next when we're analyzing data?
We usually apply statistics, so let's do that.
Turns out statistics tells us all four
of these data sets are pretty much exactly the same.
The same mean average of x and Y,
the same variance of x and Y,
the same correlation, regression, and R squared,
which is, you know, statistical stuff.
Who cares if you don't know what it means?
All I'm saying here is statistics tell us
they're pretty much identical.
So they're identical, I guess, right?
Nope. They are far from identical.
These are wildly different data sets.
Only the visuals tell you that.
So you must visualize your data when you're analyzing it.
Don't just do pivot tables.
You know, I didn't go crazy with visualizations
on that data set that we looked at earlier, but I did a few,
some distribution diagrams, a couple scatter plots.
They helped me see what's going on in the data
in a way that the statistics cannot do,
and certainly the table numbers will not do.
Why? Why does that work?
Why is it so important to do that?
The primary argument, there are really two of them,
is that literally up to half of your brain's job
is processing visual information.
Truly half of your brain is devoted to processing visuals,
which by the way, come in through your eyeballs,
which contain 70% of the sensory receptors in your body.
You are a visual creature.
On top of that, you're a visual learner.
Very important to understand picture superiority effect.
If you share text with people
and you come back and ask them what they remember
they remember a tiny, tiny little bit.
If you just add images to the same text
that retention rate skyrockets,
that's called the pictures superior effect,
a well-known psychological effect.
So you must visualize stuff to help your audience understand
and remember your content.
Now, what do we do about this?
How do we figure out what to do?
How do we identify the visuals et cetera, et cetera?
We're gonna talk more about that
when we talk about picking the right chart,
later in this course.
But I think it's important for you to identify and recognize
the four primary categories of visualizations.
And because if you think in these terms
then it makes it easier to know what to do
with your data as you're thinking
about how to use visuals to bubble up the insights
and also communicate them with your audiences.
So briefly, like I said,
we're gonna talk about this more
in module five, six, or seven.
I can't remember which one.
Number one, distributions.
It is very important during data analytics
that you look at distributions of your dataset.
I showed you in the other dataset
some box and whisker plots.
They help me see that the range of values,
what's typical, oh, the median's way down here.
And there's some outliers way up here.
How weird are those outliers?
They look pretty darn weird
'cause the median is way down here.
Distributions give me a sense of that range,
the overall population, the spread,
the clustering of the values.
As an analyst, it's good context
'cause then when I look at this movie over here
it has a number I know, is that a weird number
or normal number, right?
I know where it lives in the distribution,
that's why the power of distributions, one of the powers.
We also frequently think about visualizations
in the context of magnitudes and ranks,
which are slightly different.
When I wanna show you the magnitude of numbers,
it's about allowing you to see the actual value,
but sometimes I just need you to know the rank.
This one's in position, one, two, three, four, five,
without knowing what the actual values are.
Either way, both of those, which have,
by the way, different visuals that enable those tasks,
some overlapping ones too.
Both of those generally enable the task of comparison.
Data visualization, data analytics, data communications,
comparison is not always the number one thing.
People think of it that way.
They use that word a lot
and sometimes it is important to ask yourself,
"Compared to what?"
Almost always it's good to ask that,
but the task for your audience isn't always comparison.
Some charts are built for comparison.
Some charts are terrible for comparison.
So another major category to think about.
Another category is composition, right?
You want to understand the part to whole relationship,
the proportional share of values,
the percentage, the overall makeup of stuff in categories,
segmentation, there's a bunch of words for it.
There are composition charts like pie charts,
stacked column charts, et cetera.
There are some charts that do not enable an understanding
of composition whatsoever.
So you gotta think about it this way.
And last but not least,
for the four big categories we think of a lot of times,
especially in analysis is correlation.
When you wanna understand
are these two variables moving in tandem,
in the same or in opposite directions?
And if so, how strongly? Is it statistically significant?
Does it matter that as runtime goes up
movie quality goes up, I don't even know if that's true,
by the way it, and is it statistically significant?
Maybe there's a very slight correlation mathematically,
but does it really matter?
You gotta understand this stuff, okay?
So are you gonna use all four of those
when you're visualizing data,
especially during data analytics?
Not necessarily, but you might.
You might, especially during analytics, explore comparison,
even though your data story may end up
not being about comparison at all.
So visualize your data, it's really important to understand
what you're looking at to provide context
so that other stuff makes sense to you.
You will see stuff you would otherwise miss.
And looking at all four of these,
or at least thinking about these four categories
and which ones you do need to apply,
is a really good place to start.