Introduction Welcome! Video Lecture Transcript
This transcript was automatically generated, so there may be discrepancies between the video and the text.


16:11:16 Hi! Everybody and welcome in this video, we will have the introduction to the Institute data.
16:11:22 Science, boot, camp educational materials, I'm Matthew Osborne.
16:11:26 I will be the lecturer, for probably all of these pre-recorded lecture videos.
16:11:32 Why don't I go ahead and move us over to something?
16:11:34 You'll become very familiar with the Jupiter Notebooks.
16:11:39 Let's go ahead and share that so Jupiter notebooks are, gonna be where we have all of our lectures once you go through the first steps, you should be able to open up a Jupiter notebook environment, either through your console or through the anaconda navigator, when
16:11:53 you are able to then navigate to where you have cloned the Github Repository, for our data science Boot Camp.
16:12:02 You should see something like this, it may look slightly different for you than you're seeing on this video, depending on what changes I've made to the repository between then and now.
16:12:11 So the key thing is before you start any of these videos, make sure you go through the first steps because that's going to be where you learn how to start up a Jupiter notebook.
16:12:21 And it's also going to be where you learn how to clone a Github repository, which is what you need.
16:12:26 You need to clone the Github Repository in order to be able to interact with the notebooks like we are going to do in these videos.
16:12:33 So all of these notebooks are going to be housed in the lectures folder, and then, with in the lectures folder, you will navigate to the relevant notebook.
16:12:42 So for us, we're going to go to the introduction folder and then click on the welcome so welcome to the Irish Institute's data, science, educational content in this video there areder of the video.
16:12:55 What we're going to talk about is sort of the format of this content.
16:12:57 So as you're watching right now, they're going to be a series of lecture notebooks like what we're covering right now.
16:13:04 So these notebooks are going to have sort of just the contents.
16:13:08 So it's gonna show you both the algorithms and materials for the data science stuff.
16:13:14 It'll show you a little bit about the theory as well as a little bit of how to implement the algorithms and methods learned with you know, learned in the theory portions along with every lecture notebook, not every lecture notebook, but along with all of our lecture notebooks there, are
16:13:30 particip problem notebooks. So these are notebooks that have practice problems that cover the material that touch on the material that's covered in the various lectures.
16:13:40 So multiple lectures, notebooks may be covered in a single practice problem.
16:13:45 Notebook, but you'll see that as you start to go through them on your own, either on your own time or in our practice problem, sessions if you are attending a live version of the boot camp, so what are the prerequisites for this boot camp so the main thing is you need to
16:14:01 know python, so you need to know how to do the basics of Python.
16:14:06 You don't need to be a python, master, but you need to know the basics and particular.
16:14:09 You need to know Pandas, numpy and matte Plot.
16:14:11 Lib. If all of this sounds unfamiliar to you, go ahead and go through the Irish Institute's python, prep.
16:14:18 Materials which can be found on the Irish Institute website.
16:14:21 I haven't provided a link, because the website does change from time to time.
16:14:24 So I want this video to be applicable whenever you're watching it.
16:14:27 So just go to the Irish Institute website, and you should be able to find our python prep materials once you're there.
16:14:35 Math and statistics, so you don't have to know math and statistics.
16:14:38 I'm not assuming that you do it. Do know any of that stuff there is going to be a little bit that is touched on mainly because, as a mathematician myself, I like to provide the theory behind the algorithms as well as how to implement it if you're someone who would like to
16:14:53 know the theory behind the algorithms, but you're not as strong a mathematics.
16:14:57 Here are the things that are going to be touched upon in the theory.
16:14:59 So derivatives from calculus linear algebra, probability, theory, and statistics.
16:15:06 So not super deep in any one area. Just little bits and pieces as we need them all the stuff you'd want to know to be able to follow along is going to be covered in review slides on the Airdush Institute's website somewhere on the website there are review slides for all 4 of
16:15:21 these that you can go to, and then refresh yourself or learn for the first time.
16:15:28 If you'd like to. So, with all that in mind, with what we're going to cover as well as the sort of the format, not necessarily the material we're going to cover.
16:15:35 But the format of the of the boot camp, as well as some of the prerequisites.
16:15:39 If at any time, as you're going through the lectures, the practice problems, the sort of homework problems, anything like that, there's going to be the Errors institute slack channel that you're going to want to go to to ask questions.
16:15:54 So you can try and send a message to me on the slack.
16:15:55 You can just post a message in the relevant channel to your cohort, or there's also a channel called course Questions and Discussion.
16:16:04 Go ahead and post your questions there, and people are usually very good about getting back with an a timely fashion.
16:16:10 Either the person like myself. That's leading the boot camp will be able to answer, or someone who's taken the boot camp, or just a regular participant, should be able to answer.
16:16:18 So it's a great place to try and get help when you're going through these materials.
16:16:22 So now that you have an idea of what lies ahead, we're going to actually get started.
16:16:27 So if you can continue in the next videos, you'll see a broad overview of all the content that will be touched upon.
16:16:32 What algorithms will be learning, and that sort of thing. And then after that, you'll be ready to get started.
16:16:36 Actually learning those things. So I hope to see in the next video.