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Visualizing Data
3:47One of the best ways to get insight into your data is with data visualization. In this video we'll look at some of the more common data visualizations and talk about when to use each one.
One of the best ways to get insight into
your data, is with data visualization.
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Data visualization takes raw data,
and turns it into an image.
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This way, we can see exactly what our
data looks like, without having to guess.
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There's nothing wrong with
having your data in a table.
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But by using a good visualization,
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you can get people to understand
the result almost 50% faster.
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And it'll be about 9% more accurate, too.
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Most often, you'll see data
represented as some kind of chart.
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Let's look at a few of
the more common charts, and
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talk about when you would use them.
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First up, is a Column Chart.
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Column Charts are used to
compare different values.
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A good example of a Column Chart
would be something like a company's
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monthly revenue numbers.
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Another example,
from the perspective of a grocery store,
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could be how many of each fruit,
sold in the past week.
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However, look what happens when
we start to add more fruits.
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It gets harder and
harder to read each one.
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At this point,
it might be time to switch to a Bar Chart.
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A Bar Chart is essentially
just a rotated Column Chart.
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But it does a better job of
giving us room for our labels.
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Also, since we're used to seeing
ranked data go from top to bottom,
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if we sort our data before creating
the Bar Chart, it looks a lot cleaner.
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And paints a clear picture, that bananas
and apples are the most popular.
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The next chart we need to
know about is the Pie Chart.
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Pie Charts are used to show how something
breaks down into its constituents.
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A good use of a Pie Chart,
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would be to compare market share
of smartphone operating systems.
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An important thing to
remember with Pie Charts,
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is that you don't want to
have too many categories.
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It distracts the reader from
the rest of the chart, and
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can be difficult to see which labels,
belong to which slices.
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Once you get to six or
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so categories, it's time to start
thinking about adding an other slice.
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Another frequently used chart,
is the Line Chart.
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A Line Chart shows similar
data to a Column Chart.
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Except typically, a Line Chart
shows data that's more continuous.
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It has a lot more data points.
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For example, if a patient is wearing
a heart rate monitor that reports their
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heart rate every minute,
we'd probably want to use a Line Chart,
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instead of a Column Chart.
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Had to be a ton of columns.
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Next up, is the Scatter Plot.
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Scatter plots are used to show
the relationship between two different
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variables.
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Here's a great example of a Scatter Plot,
that shows the relationship
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between temperature and
sales figures for a frozen banana stand.
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Thanks to our Scatter Plot,
even know our data varies quite a bit,
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it's clear that higher
temperatures means more sales.
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The cool thing about Scatter Plots,
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is that they can help us make
predictions about the future.
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We can draw a line through
the middle of our data,
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to help show about how many sales we
should expect for every temperature.
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So even, though we haven't seen a hundred
degree day yet, we have a pretty good idea
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of what kind of sales we'd see,
if we were lucky enough, to hit a hundred.
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These are just a few of the data
visualizations that you're likely
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to encounter.
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If you'd like to learn more
about data visualization,
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we've got a whole course about it.
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Check out the teacher's
notes below to learn more.
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Another thing you'll want to be careful
of, is being misleading with your charts.
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It's easy to make a mistake and end up
showing something you didn't intend to.
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Or maybe you did intend to be misleading.
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Take this ad for
Microsoft Edge, for example.
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It looks like Edge is a lot
faster than Chrome and Firefox.
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But take a second to look at that chart.It
starts at 25,000 and goes to 31,000.
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So even though the difference is
pretty small, it looks awfully big.
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There's a lot of different ways charts
can be misleading or just plain wrong.
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In the next video, we'll get some
practice with data visualization.
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We'll look at the shape of our data,
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and see if it actually
looks like a bell curve.
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