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