Getting set up

- [Instructor] This course is intended for people who are already familiar with Python programming and want to learn about some of the most common tools used today in data science and engineering. So if you're not already familiar with Python, then this isn't really the right place to start. But the good news is that there are some great courses here in the library that can help. I would suggest starting with Learning Python, if you need to learn the basics of Python, if you're already familiar with Python, but want to learn about some of the language's built in data features, check out Advanced Python Working With Data. The course also comes with a set of exercise files to accompany the videos, which you can use to work through the examples in each chapter. And you can find the exercise files in GitHub at the link you see here on your screen. The course content is organized into two separate folders. The finished folder contains all of the code examples in their finished state, so you can compare your code against them as you work through the course. And the start folder contains the code examples in the beginning state, and this is the folder I'll be working in throughout the course as we build towards the finished state for each example. There are multiple ways that you can work with the exercise files in this course. Now, one way is to work directly within the browser using a GitHub code space. This repository has been set up with a GitHub code space, so you can just work directly online with nothing to install. All you need to do is fork a copy of the repository in your own GitHub account, and then fire up a code space here in the code menu and you won't need to install anything. I've already set up the code space with all the libraries and the extensions, so you'll be good to go. Now you can see I've already created a code space, and this is the one I'll be using in the course. Yours naturally will be named differently. So let me open up the code space and show you what it looks like. Now when you create your own code space, it may take a few moments to spin up, so just be patient. When the code space starts, you will see a browser based version of Visual Studio Code, and here in the files list are all the files that you'll need for the course. There's also a built-in terminal, which could be found here in the view menu. And let's go ahead and start it up. And you can see I get full terminal capability, so I'll just type Python, dash dash version, and we can see that we're using Python version 10. You're also going to want to make sure that the Python and PY lance extensions are installed. Now, these should be here for you by default because the code space is set up to use them. The second option for working online is to use Google CoLab or another online notebook, but I've tested this with Google CoLab. Now, Google CoLab is a free hosted environment that doesn't require any setup at all. All you need to do is go to colab.google in your browser, sign in with your Google account, and you are ready to go. If you don't have a Google account, it just takes a few steps to create one, and it is free to do so. You'll just click on New Notebook. That will create a new notebook like this one. You give it a name and it is ready to use. You can use the same notebook throughout the course, or you can create a new one for each example. If you want to use CoLab for this course, I suggest downloading the exercise files to your computer because I've included some example dataset files that you'll need to upload to CoLab in order to work with them, and I'll show how to do that when the time comes. Also, in a few instances, you'll need to copy and paste the starting point code for some of the examples into the notebook. You can, of course, just type the code yourself, but that's not very fun. There's also a third option, which is more complicated than the other two, so I only suggest this if you're an experienced programmer and you know what you're doing, you can download the files and work locally on your computer, and that's easy enough to do. You can either clone the repository locally or you can just download a zip file and then use your favorite editor to work on them. You will just need to make sure that you have Python installed on your computer in order to use the examples. Any one of these options, using a GitHub code space, using a CoLab notebook or working locally is just fine for following along with the course.