 So now we've come to the end of our introduction to Jmovi and it's time to talk a little bit about both what we've accomplished and what you might want to do next, your next steps. First off, here's what we've learned. We learned about installing Jmovi and sharing files and wrangling data, exploring data, visualizations, t tests, analysis of variants, regression models, analyzing frequencies, and finding factors. We've learned how to do almost everything that's necessary for the vast majority of data projects. And really, I just need to know, have you learned how to use spreadsheets before, do you know how to use either Excel or Google Sheets? Because if you know how to use spreadsheets, and if you've learned how to use Jmovi, then tool wise, maybe you're there now, maybe you have what you need. I'm firmly of the belief that spreadsheets and Jmovi cover the analytic necessities for the vast majority of people who work with data. Now, it's possible that you might need some more tools, depending on the kind of work you do, but not everybody needs it. If you're going to do something that is more advanced than what Jmovi does, and Jmovi's got a lot of capability, then you might need to do some programming for data analysis. And that usually means either are the statistical programming language are, or Python with the collection of data packages that are available for it. Those two are very, very popular in data science. And I like to use R a lot, but I much prefer working with Jmovi for the vast majority of my analyses. There are other helpful tools, if you're looking for something to add to your toolkit. First, there's SQL structured query language for data access data manipulation data cleaning. If you're working in an organizational setting, then SQL can be really really helpful. And truthfully, you only have to learn about 20 commands to get most of what you need out of SQL. Second is Tableau is a proprietary desktop application. Now there's Tableau public, which is free, but all the work you do is available free on the internet for everybody. And there's Tableau desktop, which if you're a regular person, or if you're a company is extraordinarily expensive, if you work for a nonprofit, you can get it for free. Tableau really is the tool of choice for interactive visualization, even for data scientists who are handy with code. And then third, presentation software, because remember, you do all this analysis, you're going to have to communicate it to somebody, you have to share it with them. And knowing how to use Microsoft PowerPoint, or Apple keynote, or Google slides, is going to go a very long way in terms of making your work professionally meaningful. Now, you may have seen this chart before, if you're coming from the world of data science, you've seen this is called the data science Venn diagram, and it was created by Drew Conway several years ago. And he said that data science consists of three different fields put together. At the top left is coding that's working with computers. At the top right is stats and statistics and mathematics. And at the bottom is domain knowledge, you actually have to understand the field that you're working in. And when you take the three of those and combine them, then you get data science. And it applies to a certain extent with what we're doing, even in this Shmovi situation, we've been talking about the tool, that's going to be that red circle on the top left. We have not talked very much about the statistics, that's the conceptual elements, nor the domain, that's the application. And so, even if you have the greatest tool set in the world, there are these other areas that do need your attention. And so I would say probably the single most important thing is data fluency. And seriously, reading a bar chart and a line chart is if you know how to ask a meaningful question, and know how to read even these very simple analyses to get something that is insightful and actionable out of it, you're going to have huge value in what you do. I actually believe that bar charts and line charts and scatter plots probably cover 98% of the data visualization needs for most people. The simple tools are often the most effective. It's one of the reasons I really like Shmovi, even though it allows you to do some complex things. But data fluency, and so here at data lab, we're developing courses to teach data fluency the concepts and the principles that the tools help you with, but the tools are not a substitute for them. And of course, separate from the principles and the tools is the ability to work in a particular setting. So much of the real work in any data project is not the actual working with the numbers, but as in talking with people to understand what you're trying to accomplish, and seeing how you can make sense of it and what to do with it. And the best way to do this is to work together with other people on real data projects to answer real questions. You can do that at your current job, you can do it with a volunteer group, you can do some online freelancing, any of these are great ways to get the experience you need. Shmovi is an excellent data tool, but you need to have some understanding of the statistical concepts, the data fluency, and more than anything, the ability to apply it when working with real people on real settings. So we've been talking about a tool and you have these other possibilities that we talk about at Datalab as well. But take them together. Take them together. And really, you're good to go. You've got what you need. I'm so glad you joined me here. I'm thrilled to show you Jmovi one of my favorite new tools. And I really hope that you will start finding really interesting and exciting ways to use these and getting insight out of the data that you're working with and doing something amazing with it.