 because, yeah. Switched back here. So what's the summary of today? So this course, the started alpha on days one and two, which is just about using Python. And that works in Jupyter lab in the cloud wherever. But as we're going on, we're moving further and further towards how Python interacts with the operating system itself. So basically, we're seeing how you would break out of the Jupyter notebooks and be able to run these other, like run things on your computer, run it without Jupyter, run it more times, be able to share your code, things like this. And these are the kinds of things which are needed to go to the next level. So someone, like there is plenty of work that can be done only within the Jupyter web interface, but many of you probably need to at least know of these concepts of how to go out, even if it's someone else you're working with that's managing these kinds of things. So there's feedback which is coming here. Please answer, I see right now there's 86 people who are still on this stream and this is not many answers. So please vote for what you think. Okay, so the news for tomorrow. So there's one lesson tomorrow which uses the command line and it's actually about packaging. So it's basically how you can make a package which can itself go on the pip or cond repositories and then other people can use it. But it doesn't have to go that far. I oftentimes make things into packages but I install it directly from GitHub because the use isn't really that much and I'm just using it internally myself. So the topics are more advanced but also you can do things. There's the first lesson can be done only through Jupyter lab and the third one. So these have good exercises which other people can do also. Also at the end, we have a panel discussion where you can basically ask all of us, all of us instructors anything you may want like what do you recommend for this case or that case and so on. And in fact, you can ask some good questions and try to get us debating each other about what's the right answer. Or maybe there is no clear right answer. Yeah, sometimes programs are a bit like lawyers. It depends. Yeah, and often like, I think it happens quite often in the Python ecosystem that it's basically like once you do, if you start your studies and you start your bachelor's and that sort of things and you have your friends in the same courses and you're all working on the same courses and you do the same things because those are the basics. And basically the NumPy and Pandas and those sort of things, they are the basics when it comes to scientific computing with Python. Those are like where everybody goes but then it quite quickly diverges into different fields. Like different fields have different needs and different tools they use. Like neuroimaging, they need to do 3D plotting of let's say like of MRI slices or something like that. And if you do like deep learning you do completely different tools. And of course the ecosystem is still the same and everybody's working the same kind of things but the problems become more specialized and specific for the field. And that makes it of course hard to like give a general answer what is good. But what we can hopefully provide is some semblance of like there are these kinds of like tools that go across all of these different fields. There are tools that are like ecosystem tools and these kinds of things that help you or processes like packaging in a certain way or that help you maybe even if you're working in a very specific field. But of course like every field has their own special cases that might be complicated or certain kinds of like plotting or certain kind of thing. And you need to like yeah, it's a different like specific question at that field of course. Yeah, there's some good comments here about today being advanced and getting stuck. And yeah, I mean, this is unfortunately something that happens sometimes. We have such a broad audience here. We try to do something that's a little bit interesting for advanced people and for people who get stuck you can still watch it and take it as a demo and then learn more yourself later. Because I think that's the only way to manage such a wide audience. Yeah, and like there are different ways of working in different like systems. So for example, like many of the things might be that we go to a terminal and in Windows you don't usually work with a terminal that often. Like you don't necessarily use that much of it. You don't working in a document but they're different. But in Windows, let's say you use VS Code or something like you might have a PyCharm or something and they can replicate the environment they can replicate the like with an IDE you can get even better working environment than with a terminal in many cases like you would do maybe in a Linux system. So there are different ways of working so there's no right answer. It is only answer that you want to choose from. So if you encounter problems with say let's say environment creation or some of the like command line things or scripting or these kind of things like PyCharm and VS Code you can give arguments to run when you run like press the green button to run like a code you can give it arguments to run like it would run as a script. So you can basically demo how it would behave as a script even when you're not using terminal and this is quite common in the IDE world. So there's different ways of working with the same things but of course like when we present it with the way that we most commonly see in our users which is like using the command lines as scripts and that sort of thing but the same things can apply to like different workflows as well. Yeah, so we're over time if there's no more comments from anyone maybe I'd wrap up with this one comment at the bottom. I like this course it gives a good overview of good coding practices but the course requires weeks of hands-on practice to learn and I'd say that's absolutely true and for a 12-hour course and I think any one of the lessons we teach could easily become a 12-hour course just on their own and our goal here is to show you what's possible and give you a little bit of hands-on practice of what's there but to inspire you to go learn these later on and you can learn them by doing yourself reading more or probably more likely working with other people that do things and finding it together. If you're at any of the universities that have partners you can try asking the people that are advertising the course and see if they can provide support in these things. At Ulta University we do plenty of this and I know the people in Sweden and Norway are very happy to help you with these kinds of things. So with that being said, should we wrap up? I think so. Okay, great, thank you so much. See you tomorrow, same time and yes, thank you, bye.