﻿Hello, and welcome to lesson six of our data science unit. I'm Ben, and this lesson is all to do with finishing off that analysis work that we started last lesson. But more importantly, we're going to start drawing some conclusions and making some recommendations, and hopefully making a change in your local area. So, all you'll need for this lesson is your computer, a web browser, and access to that dataset that you downloaded in lesson five. And then other than that, if you can clear away any distractions that you might have, maybe turn off your mobile phone, and hopefully, you've got a nice quiet place to work. So once you're ready, let's get started. Okay, so in this lesson, you're going to continue with the analysis section. Because last lesson, you analysed one of the questions that you posed, so this lesson is all about analysing the second question. So you're going to use the CODAP platform to do that. You're going to use that to visualise your data. And then hopefully, once you've done that visualisation, you'll be able to spot patterns, identify trends, and some outliers maybe, okay? And then once we've done that, we'll have analysed both of our questions. So we should be in a position to start drawing our conclusions, and maybe making some recommendations. Now, let's have a look at something of a related context, which is litter in schools. So I'm going to show you a visualisation to do with litter in schools, and I want you to just answer a few questions for me. So here's the visualisation, okay? Now, first of all, I'd like to pause the video and just think about these two questions. So, what do you notice about the data in this visualisation? And more importantly, what does it make you wonder, okay? So pause the video and then unpause when you've got some answers to that. Okay, now hopefully, once you've looked at this and identified that it is the recyclable waste and where it is found within a school, hopefully it's made you wonder something, okay? It's maybe made you think of some extra questions that you might have, okay? So let's narrow down the questions slightly. What I'd like to decide is, can you draw any conclusions from this visualisation? Does it warrant any further investigation? And what other information would be useful here, okay? So I'd like to pause the video again, see if you can get answers to those questions, and then we'll move on. Okay, so can we draw any conclusions from this visualisation? Well, possibly. I mean, we're hopefully looking at this thinking "Well, isn't it interesting how the PE corridor "does not have any non-recyclable waste, "it's all recyclable waste?" So that's made me wonder what that waste is. We could possibly say that there's a lot of litter in the dining hall, so we might want to investigate how many bins are in the in the dining hall? Can we do anything about that? Maybe it's some people's habits that we need to look at changing. But one thing I've also noticed is the IT corridor has a lot of non-recyclable waste in there, as does the languages corridor. So, does this warrant any further investigation? Well, yeah, I think so. I mean, I'd like to know why is there so much litter in the, particularly the dining hall? Why is there so much litter in the IT corridor, okay? Now, was there any other information that you think might be useful? Well, for me personally, I want to know where the bins are, I need to know whether or not there are bins near these places. Are there any bins in the dining hall? There's a lot of litter there, so maybe there's not. And the IT corridor as well, is there any bins nearby there, okay? So, I'm going to show you a very similar visualisation, but there's something slightly different about it. Okay, so from this visualisation, what data has been added to this visualisation? Now, now that we've added this extra data to the visualisation, what insights can we draw from this visualisation? And also, could you make a new recommendation to school management as a result of this extra data that you're looking at? Okay, so the final time, can you pause this video and see if you can come up with some answers to these questions. Okay, so hopefully you've seen that the data that's been added means the same visualisation, but with a bit more extra information, which is the distance from the nearest bin. Now you notice that's got a key underneath, and now the colours have gone to this green, but different shades of green. So the darker the green, the further away that corridor is, or the litter was found to have been, but the lighter it is, it means that there's a bin nearby. So actually, now I can see from this visualisation that if we look at the IT corridor in particular, none of the waste was found anywhere near a bin. So what should that tell us? Maybe that we need a bin on the IT corridor. So that could be some kind of recommendation that we can make to the school management. I think it's really interesting that the PE corridor has no recyclable waste, but is actually still reasonably far from a bin. So maybe I would like to investigate, well, what actually exactly is that waste? Maybe... Well, for a start, we should put a bin there, maybe just a recycling bin, but It'd be interesting to investigate this further and find out exactly what type of litter is that. For example, is it all plastic bottles? And if it was all plastic bottles, maybe we could put a plastic bottle bin there. I think the dining hall's a really interesting one there, because clearly a lot of the litter dropped in the dining hall is actually quite near a bin. So I wonder what's causing that litter. Is it a behavioural thing? Is it something that we can change? But we certainly might want to investigate that part further, okay? So hopefully this has given us an idea now that the visualisations, when we put more data into it, we can start drawing some conclusions, but also give us an extra thought, extra questions that we might have. Now, this is a really important part of this PPDAC investigative cycle that we've been talking about. Because it's not just one cycle. The idea is it keeps going around. So it may well be by the end of your investigation, you have some recommendations that you could make, but equally, there are also some further questions that you might have that might warrant further investigation, okay? So let's now move on to the actual data analysis that you started last lesson, okay? So if you remember, we used the CODAP platform to do this. So we used CODAP to upload our data that we had previously downloaded as a spreadsheet. And once we've uploaded it, we then started using the tools available to us to make our visualisations to answer one of your questions that you posed. So in this lesson, it's a case of you taking the second question and doing some analysis based on that one, okay? So what I'm going to do, if you can't remember, I'm going to show you a little reminder about how we worked with CODAP and uploaded our data and created our visualisations, okay? So if you're very comfortable with this, you might just want to watch it just to make sure you do remember. But equally, you might just want to pause the video now and start doing your analysis, okay? So I'm going to show you how to do it. So if you remember, and this link is on your worksheet, we have the link to CODAP. So that will take you to a website that looks like this, okay? Now, this is the homepage for the website, so what you have to do, is you have to click on Try CODAP, which is at the top right hand side there, you can't quite see it on my screen 'cause you can't see my cursor. But at the top right hand side, you can click Try CODAP, and that will open up this, okay? What you need to do is click on CREATE NEW DOCUMENT. Alternatively, if you saved your work from last week, you can click on OPEN DOCUMENT and just upload or open that document that you saved. But if you didn't save it, don't worry, you can just click CREATE NEW DOCUMENT. And once you go CREATE NEW DOCUMENT, to import the data, you go to that, and then you go Import and you drag your CSV file or click on here and select the CSV file that you downloaded last lesson, okay, in lesson five. Once you've done that, you'll get a window that looks a little bit like this. And here's my data that I collected. Now, you may want to resize this window, you can click on the bottom right hand side, that little green triangle there with the three lines inside, you can move your clicker and drag it down and go, it creates quite a bit of width. Now in this menu on the top left hand side, what you need to do is click on the word Graph. And when you click on the word Graph, it opens up like an empty... Well, it won't be an empty graph. It's a graph showing all the instances that you have inside your data, but this in itself, remember, the visualisation here is not useful to you. What we have to do is compare at least two variables. So I'm just going to, again, drag the bottom right hand corner out to make it a bit bigger. And if we want to put our variables into the X and Y icon, all we need to do, go to our data and hold down our mouse over the top of the title of the variable, okay? So can you see the way my mouse is over distance... Well, actually, let's just recreate the visualisation that we had before. So the visualisation we had was location. So I'm going to put location, I'm going to click my mouse over the title and then drag that into the X axes at the bottom. Okay, and there we go, it shows me the location. And the other question I had on my visualisation was, was it recyclable waste? So I'm going to put my mouse over the title, where it says Recyclable waste, I'm going to hold down my left mouse button and drag that onto the Y axes here, okay? And there we go, and it's recreated that visualisation. Now the other thing that I had in my visualisation was distance from nearest bin. Okay, so I'm going to find that, so distance to nearest bin. And this time because the X and Y axes are being used, I'm just going to put this into the middle of the graph. So I'm going to put it right in the middle of my visualisation there. Drop, let go of my mouse button, and you can see it makes the visualisation for me, puts those colours in, okay? So at that point, once you're happy with that, you can... Let me just make this a bit wider so we can see it properly. Okay. Make it nice and big, and then once you're happy with that, you can take your screenshot, you print screen, place that into your worksheet and then you can start doing some kind of mini analysis there, where you can write down a few sentences about what you think you can learn from this visualisation, okay? So this shouldn't take you too long. Make sure you select... Go back to lesson four, and in lesson four, that's where you posed your two questions. So lesson five, you analysed one of the questions, so this lesson is all about analysing that second question. So go back to lesson four, find your second question, and then you've got to create the visualisation based on that second question. And then once you've done that and you've taken your screenshot and you've written a couple of sentences about it, we're then ready to move on to the final part of the lesson, which is to start making our conclusions and making some recommendations, okay? Okay, so you finish up. Big well done, because now you finished a huge proportion of our challenge, which was to go through all the steps of the PPDAC investigative cycle. So you have identified what the problem was, and you came up with your questions, you came up with a plan for how you're going to collect the data, you collected the data, you've now analysed it. So the only thing left for us to do now is to come up with a conclusion, and hopefully make some recommendations. So the next part of this lesson is to go over to your worksheets and answer the questions that you can see on the screen now. So they are to find out what are the answers to your questions. And don't just come up with a solution, backup anything you say with the data, 'cause you have the data to prove it. So how does that data help prove the answer? Is the answer reliable? Do you think you'd need more data to really back up your argument or make a case? Or do you think that maybe when you went and collected data, it was just a bad day? Or maybe it was a good day? Do you think going out and collecting data on different days might help support your arguments? Most importantly, what can we now do with the results? So I'd like you to use the data to make a case for action, or do you think that maybe it's led to further questions that need to be answered? And it may well be you come up with a halfway house there, it may well be that you were able to answer one of your questions fully, but the second one really does require further investigation, okay? So with this, I'd like you to head over to task two on your worksheet, and I'd like to answer questions and make a recommendation to your local council about what the next step should be. And that might be to take direct action, or it might be to recommend that you found a problem but actually, this needs investigating further and what they need to do to investigate that further, okay? So pause the video now, and then complete your work sheet on task two, sorry. And when you've completed that, I'll be here when you get back. Okay, so well done, and now you've come through all the steps in the first iteration of our investigative cycle. I wonder whether or not you feel like you were in a position where you're able to make a recommendation to your local council, and if not, why not go and do that? That would be great if you could send your report to the local council and feel confident that you're not just complaining about something, but you're actually backing up something with data and making a recommendation that is fully supported by that work that you've done. Now, now that you've done that relating to litter in your local area, this was a topic area that we recommended that you did. But I wonder if there's any other areas of your life that you think investigating data could help bring around change, okay? Are there any issues that directly affect you that data could help you understand better? And how could data help you bring around that change? Now, I've got some examples on the screen here, so it could be tracking carbon emissions in your local area. It may well be you've got lots of traffic going past your school and you're worried about carbon emissions there, so why not going to take the first steps and investigate that. You could find out the registration plates for cars, and if you search registration plates on the internet, it will tell you what their CO2 emissions are. That's open data that you can investigate. You might want to bring it more personal to your life. For example, tracking your own mobile phone usage. It may well be that you're worried about your mobile phone usage and think you might be too addicted to your phone. But why don't you note down every time you've used your mobile phone and what you were using it for, and that might give you an indication of how you're using it. And maybe again, you could make a recommendation to yourself about maybe limit the amount of time that you're going to use on certain apps or certain times of the day, for example. And then finally, you could make a case for more cycle lanes in your local area. Now those are of course, just three of my suggestions, but hopefully, this whole process, this whole six lessons has got you thinking about things that maybe you think you could bring around change for, and the way you could do that and backup your argument, or learn more about that problem, is to investigate that data. So that's all for this lesson and it's all for this unit, and I really, really hope you've enjoyed it. And I've used that word empowered before. I do feel hope that you feel empowered to go out and investigate data and solve some problems out there that really could affect you and your future, okay? Now I also would really love to see some of the work that you did, I'd love to see the investigation that you've done, and if you were able to make a recommendation to your local council. Then why not let us know what your local council responded with, okay? Now if you'd like to do that, if you'd like to share your work with us, then please ask your parent 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 Ben and I really hope you've enjoyed it and look forward to seeing you next time, bye.

What change can you make in your local area? In this lesson you will complete work through the final steps of the PPDAC cycle (analysis and conclusions) and by the end of the lesson you will have used data to make a case for positiv change where you live.

Computing

Y9 Computing

Key Stage 3

Year 9

Make a change

Data Science

UNIT

Delving into data science

Global data

Statistical state of mind

Data for action

Clean it up

Lesson overview: Make a change

Intro Quiz

Video

Presentation

Worksheet

Exit Quiz

Transcript

6 lessons in Data Science: