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Welcome to lesson two of our data science unit.

Now this lesson is called global data, because in lesson one we looked at some famous visualisations by John Snow and Joseph Menard, but in this lesson we're going to look at large data sets on a global scale.

So all you'll need for this lesson is your computer and a web browser.

And other than that, if you can clear away any distractions that you might have, turn off your mobile phone, and if you've got a nice quiet place to work that'd be absolutely brilliant.

So when you're ready, let's get started.

Okay so more specifically in this lesson, we're going to recognise examples of where large datasets are used and we can relate to it in our daily lives.

Also we're going to select criteria to use on a dataset to investigate a prediction that you're going to make.

Then finally, you're going to evaluate your findings to support an argument, either in favour or against your prediction.

So, let's start off with an example that maybe you've come across before, because the image on the screen comes from Google Maps.

So I've got three questions for you, and they are, what do the different colours on this visualisation represent? Because this image is a visualisation of data.

What data is needed to show you this visualisation? And then finally, how do you think the data is collected? So I'd like to study that map in a little bit more detail and think a little bit harder about those questions.

Write down the answers on your worksheet, and when you're ready, I'll be here when you get back.

Okay so let's head back over to the questions and we'll have a quick look at, what we thought the answers were.


So what do the different colours on this visualisation represent? Well actually we can see this blue line primarily, and that blue line is the route that um, that Google has decided to make for me.

And obviously that's taking lots of data, which we'll explore in a second.

The blue, essentially the routes.

But you also see some other colours on there.

So we see this kind of amber colour and then red colour.

So what do you think that represents? Well, that actually is showing the, the real-time traffic conditions.

So with amber it's maybe more than normal traffic, so slightly heavier traffic.

And then the red is really kind of maybe gridlock, kind of heavy traffic.

Okay? So what data is needed to show you those different colours? Well first of all, to actually create the routes in the first place, Google needs to have all the data loaded of all the different routes, all the different rows, how to get from A to B, but also an algorithm's going to be put in place to work out maybe what's the most efficient route.

And if you've ever had experience at using a sat nav, or seeing one in a car, sometimes they even tell you maybe the fastest routes and maybe the most efficient routes or the most efficient for your petrol consumption or something like that.

So all that data is, is there and manipulated to form this kind of route.

Now, the other things that we're seeing there and you might have gathered with the traffic data how does it actually work out where the heavy traffic is? Well this is normally based on some real-time data.

So the car itself might have some kind of Android operating system inside it.

Or if you've got an Android mobile phone with Google services, then that might be sending location service data to Google, which is collecting all the time and analysing what the conditions are.

Cause it might know that you're in a car by the speed you're travelling and where you're travelling, but then be able to work out what speed you're travelling at and compare it against what's normal.

Now if we're comparing it against what's normal, then obviously it needs to have some kind of historical data in there too.

So it's not just measuring that real time data, it's working out maybe the busiest times of the day and when that road is likely to be at its most busiest.


So let's move on.

Now again, we've already looked at a large data set.

You imagine how much data Google needs to collect to produce a map in the first place, let alone the traffic data or work up the routes.

It's a huge amount of data being collected from all its users that have got location services enabled.

So what I wanted to do is show you another video from another service that you may have heard of before, which is Netflix.

Now Netflix is an online TV and movie streaming service, but they're collecting data all the time.

So this video is going to highlight to us what kind of things they do with the data, and why it's important for them to collect the data and what, how they analyse it.

So just take a moment to watch this video now.

Okay so I find that really fascinating stuff.

And it's exactly what data science is.

Netflix is taking data all the time from all of its users and working out what they're watching, when they're watching it.

And it's fascinating to know that, that data science in action there is pulling all of this data out, and enabling us to have some insights, and therefore make some decisions.

So clearly Netflix are using that to make a decision about what their next TV show is going to be, or they can pretty much guarantee they know what's going to be a hit or popular with their users.

So that's a really great example of Netflix, also data science being used by Netflix in action.

So what I might do is pause the video for a moment and then think about what other companies can you think of that might collect large sets of data.

So we looked at Google, we've looked at Netflix, what other companies might be collecting huge amounts of data from us all the time and using it to provide some kind of insights or make decisions with that data.

Okay so pause the video and then un-pause when you've got a couple of examples.

Okay so what examples did you come up with? Now there are lots of examples you could have used.

The one that really kind of sticks out in my mind, I think like social media companies who might be collecting data about what kind of things you like and dislike and the kind of groups that you might join.

But maybe also there are things out there called smart cities.

Smart cities are really fascinating things, because what they're doing is collecting data all the time about maybe things like people's movements around the city.

And they use that to determine maybe, what bus routes are going to be most popular, or where do we need to have bus routes? Maybe there aren't bus routes.

So all those kinds of data is being collected in those cities to help, you know, improve people's lives.

So now we're going to look at a global data set that you're going to take action on, and you're going to have a go and play around with and experiment with.

So what we're going to do is decide, look at some global data and I'd like to decide or predict in advance where you think the best place is in the world to live.

So we're going to look at this large data set that compared countries across the world, in order to help us work out the answer to the following question, where is the best place in the world to live? Now I've already got a thought on that.

And I wonder whether or not some things or countries are kind of going around in your head now.

Now I picked a picture of a really nice place that I'd like to live.

This is actually Fiji, but I've got an idea in my head.

I would really like to live in Barbados.

Because I think Barbados sounds like a wonderful place to live.

Now most of us, we like the idea of living somewhere like Fiji or Barbados, but would it actually really be the best place to spend the rest of your life? So in order for us to gain an answer for this, we need to really think about, what the things are that are important to us, of a place that we'd like to live.

And what conditions would we like to live in? For example what's the life expectancy in the country that we'd like to live.

Maybe what's the average income or the wealth of people there.

What's the health conditions of them, if they've good health care.

And maybe also one thing that's important to me is CO2 emissions.

You know is it low CO2 emissions and not many people using their cars? So those kinds of things are important.

Can you think of any other things, other than life expectancy, income, wealth, health, and CO2 emissions that might be important to you? Well I've come up with a list of extra things, but if you've got some extra criteria that's not on my list then that's fine, but try when you can to pick three from, from this list, cause these are the ones that we can explore using the software we're going to look at and the data set that we have.


So I've got life expectancy, average health- income, health, CO2 emissions, but the new ones I've now added, maybe the average temperature, because I wouldn't want it too hot.

I want it hot, but not too hot.

Inequality, is it a fair society? Unemployment levels, population density, do they allow freedom of speech, and what's the crime rate there, you know, what's the number of murders per hundred thousand people.

Okay? Cause we won't want that to be very high, because I certainly want to live, won't want to live in a dangerous place.

Okay so just pause the video for a moment, and then see if you can pick three from this list.

And then un-pause when you've got three that you feel comfortable with.

Okay so I wonder if your three were the same as, as mine.


So what we're going to do is going to use a website called Gapminder.

Okay so we're going to use a data visualisation tool called Gapminder to help you decide where the best place to live is based on the criteria that you've set.

So your criteria should be those three, three things that you thought were most important.

Okay so you're going to use that criteria to investigate your prediction.

Okay and you're going to document your findings on your worksheet.

But before we do that, I'm going to give you, I'm going to head over now to Gapminder, and give you a bit of a demonstration about how to use the software so we can confirm or deny our, our opinions on that.

Okay so this is what your worksheet is going to look like, when you get to it.

Now the first thing we have to do is list down or we're going to, going to ask you to list down the three sets of criteria that you think are most important.

Okay and then underneath that, you're going to make a prediction of where you think the best place in the world is to live.

Now like I say, I hope that Barbados is the best place to live, and I'm going to pack my bags and go move.

Okay now the two websites I'd like to use, and please ask permission from your parents or carer before you access these websites, but they are Gapminder and Berkeley Earth.

Okay so first we're going to look at Gapminder, and then I'll show you the second one as well.

So I'm going to head over to Gapminder now.

And this is what Gapminder looks like.

Now each one of these little bubbles represents a different country in the world.

And on the right hand side, we can see a list of countries.

So all you need to do is search.

You can do a little search for your country there, or you can scroll down til you find it.

So I'm going to click on Barbados here, so I'm going to get that little tick, and see what that does once I tick that, I go and put the wrong one there, I seem to have got rid of, I've got Bangladesh.

I going to see if I can de-select that, I was going to click de-select.

Right okay so I'm going to click Barbados.

So Barbados is now highlighted for me.

Okay so you can see that Barbados is there, okay you can still see the other colours in the background, which is kind of helpful to us because it does help us to compare the different countries.

But if you wanted to kind of get it so that it only showed Barbados and, and because it's such a small dot because they thought, so represent in comparison to population size.

So the bigger the dot means the larger the population.

Barbados has got quite a small population, that tells me something for a start, but it's a bit kind of disappeared there behind China.

So if I wanted to get rid of those dots, I can use a slide at the bottom left there, and move it down so I can only see Barbados.

But like I say, it might actually be helpful for us to compare it cause of the country.

So I'm just going to move them an ounce, a slice.

So you can still see it.

First of all, this is comparing life expectancy and income.

Okay now life expectancy was one of my criteria goals.

So let's have a look at that.

Now if we look at Barbados there, you can see it kind of makes up the dotted line and actually the average, the life expectancy of Barbados is 77.

3, okay.

Now we might want to compare that against where we live now.

So let me scroll down and find the United Kingdom.

Okay so I'm also going to put United Kingdom.

So it compares Barbados to maybe where I live now.

So I can see that that is 77.

3, and this is 81.


So I'm going to live on average there's more of a chance I'm going to live longer if I live in the United Kingdom.

Okay so, it's up to you, whether or not you think that's a good thing or a bad thing, but either way it allows us to make that comparison.

So let's say we're looking at something that's not life expectancy.

What we can do is click on this arrow here.

Okay and then we can pick something out.

For example if you go through all these things, these are all the different categories.

So you need to find the one that you saw, selected as your category on this list, but let's say it's CO2 emissions.

Okay so again I'm going to click on this now, and it will get rid of life expectancy now, and it should put in a CO2 emissions in a second.

There we go.


So CO2 emissions in Bar- in Barbados here, okay is three- Oh, that's Mexico sorry.

See if we can get Barbados.

There we go.

So 2014 was the last time it was measured and it was 4.


Okay where's the United Kingdom? It's 6.


So it's definitely higher in the United Kingdom.

Okay so maybe that's a, that, that proves that maybe that would be a better place to live, the United kingdom.

But clearly there were dots on the right-hand side, which are much much better such as Luxembourg and Singapore.

Okay so anyway, we can, we can check that out and think, you know, is that a good thing or a bad thing.

Now so that's an example of how we can use Gapminder.

The other one was this one which is Berkeley Earth, and that's going to show what the average temperature is only.

Okay so let's just find Barbados.

So let's select Barbados.

Okay and it should show me this graph.

Okay perfect.

Now so I can see the graph is going to show the average temperature, and obviously the last time it was recorded was 2019, which is 27.


Okay now what other information can we get from this graph? That's really interesting, isn't it? Because it is showing this trend line here, and you can clearly see the trend line is going up.

Okay so once we've picked these different criteria for us to explore, what I'd like to do is continue to complete your worksheets, by taking each of those three criteria, I'd like to tell me what criteria you've investigated, I'd like to maybe take a screenshot if you can, of the graph that you've found.

And then write just a little short something about what your findings are, and does that support your prediction.

And let me give you an example.

If I ever had back over to the slides.

Okay, what I've got here that you can see I've taken a picture of the average temperature.

So I'll put the criteria investigated was the average temperature.

And what I thought my findings were I said, the average temperature was 27.

65 degrees Celsius in 2019.

So this is an, I think this is a nice temperature but I wouldn't want it any hotter than that.

So the graph shows that the trend is going upwards, so therefore it's likely to get hotter in the coming years.

So does that make it the best place to live or would I want to live somewhere that's going to be slightly cooler or maybe more of a stable temperature.

Okay so that's your task now, so if I head back over to, I'd like to pause this now, go to task two, so remember I'd like to write in your criteria that you want to investigate, and then I'd like to document your findings by taking a screenshot and then writing just a small amount of text to see whether or not that graph, that visualisation, helps support or disprove your theory.

Okay so un-pause when you've done that, and we'll carry on with the rest of the lesson.

Okay so well done on completing that.

What's the country you predicted the best place in the world to live? I'm not so sure Barbados actually was the best place in the world to live but, certainly a great place to visit on holiday.

I think looking at the data maybe Singapore looked like a nice place to live but, you know what? I'm really happy here in my office and my shed in the United Kingdom right now.

So well done in completing that anyway.

I hope you enjoyed that lesson and really look to see the work that you've done.

And if you found the best place to live in the world, then definitely prove it to me.

Okay so if you'd like to share that with us then please do, ask your parents or carers to share your work on Instagram, Facebook, or Twitter tagging @oaknational and using the #learnwithoak.

Okay so that's all for me, I'm looking forward to seeing you next lesson.