Intro Quiz: Global data

Video: Global data

﻿Welcome to Lesson 2 of our data science unit. Now's this lesson's called Global Data. 'Cause in Lesson 1 we looked at some famous visualisations by John Snow and Joseph Minard, 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 data sets are used that we can relate to in our daily lives. Also, we're going to select criteria to use on a data set 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 you 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, okay? 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 Google has decided to make for me, and obviously that's taking in lots of data, which we'll explore in a second, and the blue is essentially the route. 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 us 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 roads, how to get from A to B, but also an algorithm's going to be put in place to work out maybe what is the most efficient route. And if you've ever had experience of using a sat nav or seeing one in a car, sometimes they even tell you maybe the fastest route, and maybe the most efficient route, or the most efficient for your petrol consumption, or something like that. So all that data is there and manipulated to form this kind of route. Now the other data that we're seeing there, you might've 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'd need 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. Okay, so let's move on. Now, again, we've already looked at a large data set. If you imagine how much data Google need to collect to produce a map in the first place, let alone the traffic data or work out the routes, that's a huge amount of data being collected from all those 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 and well, they're collecting data all the time. So this video's 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 how they analyse it. So just take a moment to watch this video now.

Today Netflix is a leader in the television industry with other 93 million viewers around the world streaming billions of hours every month. And Netflix is watching and collecting data on every second and it all starts as soon as you log in. Once you log into your profile, Netflix's algorithms go to work collecting data on how you scroll through their recommendations until you pick something to watch. And once you decide what to watch, whether you fast-forward, rewind, or re-watch a scene, and even what device you're watching on- and a lot more. And Netflix does this every time you log in from the very first time you sign in, and all this data gets collected into your user profile, and as you continue to watch, Netflix suggests its recommendations and sorts you into groups of users with similar viewing patterns that they call communities. There are various communities, some more specific than others, and once sorted, Netflix can take users from communities and compare them to each other to recommend content that they will likely enjoy. But Netflix doesn't stop there. They have been looking ahead using the same data to project future programmes. In 2012, Netflix bid $100 million on two seasons of the US version of House of Cards, outbidding the likes of HBO, AMC, and Showtime, a pretty big risk for a company that had never produced their own original series before. But with all the data Netflix had collected, they could see it really wasn't as much of a risk as it seemed at the time. And if we look at the data, we can see that Netflix knew that the UK series of House of Cards had a decent viewer base already, and that political dramas were really popular in general. They also knew that Kevin Spacey's films had always done well over the years, as well as David Fincher's rise in popularity at the time. When you put all this together, a hit was all but guaranteed and Netflix has been on a roll ever since, winning 22 Emmys since 2013. And Netflix continues to produce content using this method, producing more and more shows their viewers and critics both love and crave.

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 data science in action there is pulling all of this data out and enabling us to have some insights and therefore make some decisions. And 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, or sorry, data science being used by Netflix, in action. So what I'm going to 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 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 insight 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've used. The one that really kind of sticks out in my mind is things like social media companies, who might be collecting data about what kind of things you like and dislike, the kind of groups that you might join. But maybe also there are things out there called smart cities. Now smart cities are really fascinating things because what they're doing is collecting data all the time about maybe things like people's movement 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 being collected in those cities might 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 an experiment with. So what we're going to do is decide, look at some global data and I'd like you 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 compares 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 thoughts 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 really 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, is there good healthcare? And maybe also, one thing that's important to me is CO2 emissions, you know, is it low CO2 emissions, are not many people using their cars? So those kind 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 where you can to pick three from this list, 'cause these are the ones we can explore using the software we're going to look at and the data set that we have, okay? So, I've got life expectancy, average income, health, CO2 emissions, but the new ones I've now added are maybe average temperature, 'cause I wouldn't want it too hot, I'd 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 100,000 people, okay, 'cause I wouldn't want that to be very high because I certainly wouldn'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 mine. Anyway, so, what we're going to do is we're going to use a website called Gapminder, okay? So we're going to use a data visualisation tool called Gapminder to help us decide where the best place to live is, based on the criteria that you've set. So, your criteria should be those 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. Before we do that, 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 opinions on that. Okay, so this is what your worksheet's 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, I'm going to ask you to list down those 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 and 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? But first of all we're going to look at Gapminder, 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. Now, on the right hand side, we can see a little country. So, all you need to do is search, you can do a little search for your country there, or you can scroll down to find it. So I'm going to click on Barbados here so I'm going to give that a little tick. And can you see what that does, once I've ticked that? I've clicked the wrong one there. I seem to have got rid of- I've got Bangladesh there, so I'm going to see if I can de-select that. I'm just 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 the color's such a small dot because the dots represent in comparison to population size, so the bigger the dot, it means the larger the population. Barbados has got a quite small population, so 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 the slider 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 against other countries, so I'm just going to move them in ever so slightly so you can still see it. Now 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 that dotted line, and actually, the average, the life expectancy in 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 in the United Kingdom. So it compares Barbados to maybe where I live now. So I can see that that is 77.3, and this is 81.1, so I'm going to live, you know, on average, there's more of a chance I'm going to live longer if I live in the United Kingdom, okay? So, I dunno, 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 else- for example, if we go through all these things, these are all the different categories, so you need to find the one that you selected as your category on this list, but let's say it's CO2 emissions, okay, so we're going to click on this now, and it'll get rid of life expectancy now, and it should put in CO2 emissions, in a second. There we go, right, so, CO2 emissions in Barbados here, okay, is 3- oh, that's Mexico, sorry, see if I can get Barbados, there we go- so 2014 was the last time it was measured and it was 4.47, okay, where the United Kingdom is 6.42, so it's definitely higher in the United Kingdom, okay, so maybe that proves that maybe that'll be a better place to live than the United Kingdom, but clearly there are dots on the right hand side which are much, much better, such as Luxembourg and Singapore, okay? So, anyway, 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 us the average temperatures only. Okay, so let's just find Barbados. So we're going to select Barbados, okay, and it should show you this graph. Okay, perfect. Now, so I can see the graph, it's going to show me the average temperature, and obviously the last time it was recorded was 2019, which is 27.8, okay? Now, what other information can we get from this graph? It's really interesting, isn't it, because it's 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 worksheet by taking each of those three criteria. I'd like you to tell me what criteria you've investigated, I'd like you to maybe take a screenshot, if you can, of the graph that you 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 of that. If I head back over to the slides, okay, what I've put here, as you can see, I've taken a picture of the average temperature, so I've 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 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, therefore it is 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 this, I'd like you to pause this now, go to task 2, so remember, I'd like you to write in your criteria that you want to investigate, and then I'd like you to then 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, helped 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. Was 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 made Singapore look like a nice place to live, but, you know what, I'm really happy here in my office, in my shed in the United Kingdom right now. So, well done on completing that anyway. I hope you enjoyed that lesson, I'd really love to see the work that you've done and if you've 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 carer to share your work on Instagram, Facebook, or Twitter tagging @OakNational and using the hashtag #LearnwithOak. Okay, so that's all from me, I'm looking forward to seeing you next lesson. Okay, bye!

Worksheet: Global data

Exit Quiz: Global data

Completed: Global data

In this lesson, we will explore how ever-improving advances in technology have made it more feasible to collect, store, and analyse much larger datasets than previously. We will then look at global data sets, make predictions, and use visualisations of the global data to prove or disprove your predictions.


Computing

Global data

Introductory Quiz

Video

Worksheet

Final Quiz

Lesson Complete