-: Hey, everybody.
So this week we're here to talk
about an introduction to thinking like a communicator,
an introduction to the whole class overall.
And I think it's worth, always,
stepping back and asking ourselves, "Why?"
Why are we here talking about this,
this entire course, Data Visualization and Communication.
Why is this important?
Why should we bother?
And the short answer, briefly, really is this.
Now, normally in a class, I would ask,
"Does anybody recognize this person?"
And maybe one out of 20
or 50 or a hundred would know.
In my world, in the data visualization world,
he's very, very well known.
But for normal people, not so much.
His name was Hans Rosling,
and he was a Swedish doctor, a physician.
Also a public health professional.
And he became very well known
in the data visualization world during, I think,
like the early 2000s, early to mid-2000s.
He did a bunch of TED talks and BBC specials,
and you know a bunch of YouTube videos.
You can find him if you just Google his name.
Just doing data storytelling,
explaining data in a very compelling, clear way.
So I'm gonna play one of these videos for you.
Let's just watch this,
and then we'll circle back.
Hans: The world my father told me about,
50 years ago, was a divided world.
It looked like this.
Each bubble is a country.
Size is population.
Blue, Africa. Red, Asia.
Yellow, Europe. And green, the Americas.
Vertical is child mortality.
From 30% of children dying before the age of five,
down to almost zero child death.
Horizontal, number of babies born per woman.
From eight to less than two.
But most countries were up here.
Women had six to seven children.
Child deaths were frequent.
Almost every family lost one or more children.
In many people's mind,
the world still looks like this:
developing and developed.
But it's a myth because the world
has improved immensely in the last 50 years.
Here we go.
Year by year, child mortality fell
in almost all countries.
And as child mortality failed,
women chose to have fewer and fewer babies,
and that enabled them to invest more time
and resources in each child.
By 1990, some of the so-called developing countries
had already made it down here.
Some were in-between,
and a few remained up here with very high child mortality.
Ethiopia had hardly moved at all.
It had passed through decades
of famines and political turmoil.
Many people think that Ethiopia is still stuck up here.
But look what happens after 1990.
With improved access to health service
in rural areas and well-spent aid,
child mortality falls dramatically in Ethiopia.
And with better access to family planning,
women choose to have fewer and fewer babies.
Ethiopia has come halfway
and is moving quickly down to this corner.
But Ethiopia still faces many challenges.
I will split the Ethiopian bubble.
The capital, Addis Ababa, is already down here.
But the remote Somali region of Ethiopia
still have high child mortality.
But most of the regions,
90% of the population are centered around the average.
Most people think that the problems
in Africa are unsolvable,
but if the poorest countries can
just follow the path of Ethiopia,
it's fully possible that the world
will look like this in 2030.
Then, there will be no countries left in the box
we once called the developing world.
But to ensure that that happens,
we must measure the progress of countries.
It's only by measuring,
we can cross the river of myths.
Professor: So that's Hans Rosling
being classic Hans Rosling.
And you think about the data here,
we only have four variables, right?
Child mortality, babies, born per woman, okay.
We have population size. That's the bubble size.
He doesn't even talk about that.
But it helps us track like India and China,
a couple of the bigger countries.
And then, we also have the continents, right?
Just the color to show us Africa, Asia, et cetera.
But underlying that is very complex data, right?
We have 8 billion people on Earth today.
50 years ago, 60 years ago now almost,
how many were there?
5 billion? 6 billion?
Whatever it is, times 50 years,
whatever the average is.
You know, we're talking about many, many,
many, many data points, right?
We're talking about very sophisticated statistics
to really understand even these two simple variables,
four, really,
and how they've changed over time,
and especially the projections
of what it might look like by 2030.
So serious underlying math and statistics going on.
But he explains it very, very clearly,
and he doesn't just throw a chart at us and say,
"Good luck. Hopefully, you'll understand it."
Or have a paragraph of text explaining
sort of the meaning without really walking us through it.
No, he explains it in a story.
And the story, the narrative, flows really nicely.
And Ethiopia is an example
that he sort of brings up
to really illustrate the idea behind this story.
And he even gets into the nuance
of Ethiopia to explain that,
"Listen, it's not all black and white, right?"
So, why are we here?
That was where we started this question, right?
We are here because we want to be more like Hans.
We want to work to take
what may be very complex underlying data,
and find insights in it.
Not that we're teaching how to find the insights much,
a little bit last week.
But then, most importantly,
for our purposes in this class,
to then take those insights
and communicate them to audiences.
Get our audiences to understand,
really connect with that information,
so they can do whatever they're supposed to do with it.
Maybe they're just supposed to learn something.
Maybe they're just supposed to teach it to others.
Maybe they're supposed to take some sort
of action based on it.
Whatever it is,
great data communicators can really do that.
So that's why we're here.