 Excellent. Thank you. I'll start things off and we can do a super quick introduction. I'm an educator from Alberta, Canada, just east of Edmonton, currently teaching grade five, but I've taught a number of subjects and also been outside the classroom on a few different projects. And we're here to talk about expanding students' worldview with data science in social studies. And, Marina, if you want to introduce yourself and what we're talking about. I'm Dr. Marina Roberts. I teach at a K-12 district outside of Calgary called Rocky View Schools, and I teach humanities, science, or sorry, staring at science, humanities, which is social studies and English courses. I also teach as a Session Instructor with the University of Calgary, the University of Victoria, and I'm a UBC learning designer in the middle of this COVID crisis to help instructors get their courses online at the university level. But today we're going to specifically talking about how can we integrate data science in particular into social studies. Go ahead, David. Sure. Thanks. So looking at globalization, geography, government systems, economics, all of those sorts of things and using actual real-world data to look at trends, comparisons, that sort of thing. And so we'll look at some data sources. And some of those have tools built into them. Gatminder and Wolfram Alpha, definitely. Wikipedia, you may have heard of. That's a great source for data. And we'll look at some open data portals and things like that. And we also have some curriculum aligned. They're aligned to curriculum in Canada, but probably aligned to other places as well that use data science specifically to introduce social studies concepts. And it's all creative commons attribution as is this slide deck. And feel free to use any of it to remix and all of those wonderful things. The Calisto project is really what brings Verena and I here. We are colleagues in that project and that is funded by the Canadian federal government through the CanCode project. And it's a collaboration between technology accelerator known as Cibera and the Pacific Institute for the Mathematical Sciences, which is a consortium of university math professors promoting that sort of thing. And so these two nonprofit organizations are collaborating together on the Calisto project, which is about infrastructure and learning resources. And this doesn't need to be a Calisto commercial. It's a project if you want to check it out that is about incorporating computational thinking and data science into regular curricular outcomes, looking specifically at kind of grades five to 12, but in any sort of learning environment. And so the reason we're looking at data science as something important in social studies, but important in the world in general, is that really data science is about finding and communicating things from data. So we are awash in data in our world these days. We're generating more and more data and collecting and consolidating more and more data. And so being able to find things out from that to change our worldview based on that to perhaps even make predictions about things or even if we get far enough to make prescriptions to say, if we change this thing, then we predict that that this better thing will happen. And so that's kind of the the importance of data science in a humanities context that we have here. Yeah. And when I came in to help as an educational specialist with Calisto, I really wanted to think about how does this stereotypical math and computational thinking really connect with humanities and doing some research and looking at the current curricula is about connecting the math and the emerging curricula with literacy, specifically what we call data literacy. And when we look at data science as very interdisciplinary, just like what we do in humanities all the time, it uses the power of the internet to explore big data sets and really understand them. And and also there's a focus on telling stories with data visualization. This is done through the data collection, which considers bias, filters, data, chooses data and tracking data. But most importantly, it's about how do we communicate meaning, the literacy part? How do we choose how to communicate this data or how this data works, interpret this data? And that's what we call multi and transliteracy. Oh, so we're going to start by there's phases and a continuum, really, when we're thinking about data visualization and data literacy. So first you start with interpreting the visualizations and then we get into creating visualizations with scaffolding. And we'll talk about that a bit as we go along too, because I quickly learned that there was no point in me just jumping in to the Jupyter notebooks, for example, without really understanding how they were created and what we do to put them together. So we'll start with go ahead, David. Sure. And there are a few different ways we can create visualizations of data sets. And we can definitely do this by hand. We have students doing this all the time. They draw bar graphs on paper or things like that. And we can we can do them digitally as well by hand as in without programs that are designed for data visualization. You're familiar with spreadsheets likely we can use Excel and all of those other great things to you to create visualizations. But really what we are finding, we being society is finding is that using coding languages, Python or JavaScript or other things like that, that allows for much more reusable visualizations that we can take these either really big data sets or changing data sets or data sets from multiple different places and join them together into visualizations that are repeatable, that we can say, here's the data set. Here's how we created the visualization and here's the visualization. So then people can go back and OK, well, I'd prefer if this thing was blue and I'd prefer if it showed this date range and all of those sorts of things. So using Python, it is my favorite language and tends to be the favorite language of data scientists as well. Within a Jupyter notebook, and we'll talk about Jupyter notebooks in a little bit, but basically that's a platform that allows us to intersperse text with code and visualizations and put this all together in a in a way that can be can be published and can be reused and and remixed and all of that. And of course, the importance of visualizations is to be able to look at data rather than just a big table of data that doesn't seem to make a whole lot of sense, we can see that if the data set looks like this or these other ones at the top right, these four at the top right with the orange dots, those all have the same statistical values, so they'll all have the same median and the same slope to that best fit line and such. But obviously from looking at the visualizations, you can tell they're very different. And then we have here that similar to visualization that became popular in the US with their election that just happened or is sort of happening, someone made a similar one in Canada based on our last federal election looking at basically the fact that geography doesn't vote, people vote. And so even though it looks like there's this huge section up here that is red, the there aren't a lot of people that live up in the far north of Canada. There are the areas where people live are more down in the south. Not that we really like to be close to Americans, but we really like to be closer to warmer climates. I think it's probably the reason for that. But but again, the importance of visualizations to be able to say this is what we are seeing from our from our data sets. And I think some of the examples that we have that we can use right away are in this series is through the New York Times and it's called What's Going On in This Graph. Thank you, David. And I pulled together the list of all the examples and we'll just click on one of them right now. But what it does is it gives you a wide variety of topics that you can use as a teacher in any classroom. This is David's favorite one. I should tell that he was going to bring this one up. I think you love this one. It creates a visualization for you on the top. It goes into some detail explaining the visualization and then below it asks you questions about it inquiry based questions to really start thinking about the data in different in different ways. There are also ways for it to be participatory and open. And when we think about open educational practices, because we can connect these students all over and their answers and the different varieties, now there you'll see that most of the graphs are different, so they're not all the same. So that first one was about coal. You can make some inferences with it. But they're all different ways. And there was one that we found yesterday and it was a starburst. We were talking about how how engaging it was to have these different types of data. The good thing about this or data visualizations for me is then the students start thinking, how can I make these kind of digital and data visualizations? So we keep like and we have started this actually with our data visualization of the week with the Callisto project as well. But we're just our number week, number two, I think we are. Thank you, GapMinder. Go ahead, David. Yeah, so GapMinder is one of my favorite websites in terms of data informed worldview. So this is their sort of famous tool where you can change the X axis. And here's the default one is showing life expectancy as a function of income over time. So if we look back, you know, eighteen hundred is probably as far back as their data goes. We can see as time goes on, the different countries tend to move differently in terms of income and their life expectancy. But many of us seem to be stuck in the worldview of, you know, the 1960s of we've got developing nations and then we've got developed nations, but, you know, developing nations where the income is low and life expectancy is low. But what we see is that as we actually go along through time, things tend to be moving up and to the right. Life expectancy has gone up all around the world. Income has gone up in a lot of places. I mean, the African continent here, there's still quite a few that have a much lower income, but even then life expectancy has gone up. And we can say if we put the child mortality on the Y axis, we can see a similar sort of trend that thankfully child mortality is decreasing over time as we look through these. And so students can visualize these data sets and they can look at how. Where do we go from here? Anyway, it explains on the site where the data sets come from, how they collected them and the data sets are almost all creative commons or open in some sort of way for us to use in in different ways. So GapMinders is a great website for that. The founder of this hands rolling has a great TED talk or TED talk and a number of videos. And he's much more enthusiastic or was much more enthusiastic about this. And so he's he's brilliant to watch and and to hear about using data as a visualization tool to really change our worldview to say there isn't really a gap between, you know, that the first world and the third world or whatever terms that we use to use those sorts of things. And then also on the GapMinder website is a tool that my wife, who teaches grade three, really likes to use because in grade three, they talk about different places around the world and how cultures are different and how things are the same around the world. Dollar Street, I'd love their tagline photos as data to kill country stereotypes and what they've done is they've taken a bunch of pictures of different places around the world and correlated them to monthly income. And what we see is that a family who makes nine hundred and sixty dollars a month in China would have a very similar sort of lifestyle to a family that makes nine hundred and sixty dollars a month in the United States. And so seeing showing that income is probably a better correlation or a better predictor of quality of life than geography is. And then you can also look at on the chart here, you know, what is the if we narrow it down to just sort of this middle right above the middle bit, we can see, OK, well, that's where it might start to look more like what the students in our classes are experiencing, perhaps, or to have this discussion with students to say, OK, well, where where do you think we fall and then start to look there and what do what did their lifestyles look like? And how does that compare to yours? And so they have for each of these they have different pictures of this is what their kitchen looks like and this is what their their bathroom and their bedroom and all of those sorts of things. So again, using data visualization as a visualization that we don't often think about just using photos on the Dollar Street project, which I think is is very cool. And so infographics are similar using photos or pieces of photos or other sorts of things, and we're familiar with infographics as a way to to implement data visualizations. We're just going to say that Google Trends is another way to look at analytics and data analysis. And then another example, if we go to the next slide, if we're thinking about data biographies like do biographies and social studies, we can do data biographies following this seven step format that you can learn about more through the night center courses. This is more focused on journalism, though, and data journalism. So that kind of perspective. Then we keep going and we have some online hackathons where we actually do storytelling and action. For example, we go off to Mars and think about how could we live on Mars and look at data sets and we support our teachers with that. And we have an online hackathon planning tool that we developed with numerous people around the world in order to figure out how teachers can support and offer their own online hackathons or alternatively, especially if they're in Canada, we can work with them and support them. And I think we want to get to that last bit, which are the Jupyter Notebook. So now we'll get into actually how you do this. How can you teach this? Yeah, absolutely. So Jupyter Notebook, as I mentioned, is where you have cells of text interspersed with cells of code. And we're running a Callisto hub that is freely available. You just need to have Microsoft or Google account to sign in with that. Google runs one IBM has their Watson Studio, which has a lot of cool features to it. Or you can just run Jupyter Notebooks in program on your own computer. If you install the Anaconda program, then you can run them. But if we look at the Callisto hub specifically, we can and I think I'm not logged in anymore. So just quickly logging in there and we'll create a new. Python three notebook. And so we can have cells in this notebook and cells. If we have a text cell like that and then this is code that we can then run right within it. And if we want to do some visualizations, here's enough code to be able to make a bar graph. So using an online or a Python library, which is code somebody else has written, and then putting in some data about whatever it is that we're wanting to visualize, adding a title to it and then the magic line of creating a bar graph and showing it. So if we run that, then we get a bar graph that is just an image that can be put into something else if we wanted to change this to some other language and change the numbers. And we see that it will change the bar graph when we run it. And so we can give students this much code and say, OK, now find your own data that you want to make a visualization of and use this to create something. And we can make pie charts. We can use online data pulling a table from Wikipedia with just four lines of code here to generate this sort of visualization. And then we have lots of great examples of larger scale visualizations. So geography or residential schools, for example, takes a look at where all of the residential schools were across Canada. And it creates an interactive map that you can click on, zoom in and things like that. The globalization notebooks are quite interesting. They take large data sets around. So, for example, music takes two years of Spotify data from around the world and and compares how things are in different countries and such. So using these big data sets from places like Statistics Canada, Open Data, GapMinder, all of those sorts of places to create these visualizations. So to get started, if you want to use the Callisto Hub, you can do that callisto.ca slash get started. Or you can definitely use any of our resources right from GitHub and run them on your own server or anything like that. So, Rena, was there anything else we need to talk about in the 45 seconds that remain? I just wanted to add the main reason that I got involved in this was that it is is open learning in action because you are learning how to remix. You're in a culture of open source. You're with collaborative colleagues who spend all their time remixing and updating and giving you feedback. So that's why I definitely think it's something that K-12 teachers in particular should get involved with. Absolutely. So we have our contact information there, email and Twitter, and then, of course, you can keep a copy of this slide deck and all of the other R's that relate to it. And there are four or five of those R's, aren't there? Five, just to help you out there. Four or five, how many are there? That's why I helped them out. Thank you, guys. Thank you so much. This is fascinating. I wanted to keep on going. So important to be able to visualize information in this way. And I'm thinking that it also breaks language barriers and geographic barriers and being able to express information a different way. This is just fascinating. Thank you. Thank you so much for sharing.