Hello and welcome back to the intro to Data Science Course, and today we're going to talk about a brand new concept that we're introducing to the world called the six week data scientist. We have seen lots of success stories. We've been through the journey ourselves in some cases several times. And we know what it takes to become a data scientist. And our statement here is that you can become a data scientist in six weeks. You can become a starting distance, not promising that you can become a super successful best data science in the world. No, but you can become a data scientist that can add value to businesses, that can apply machine learning visualisation, understand statistical concepts, and use Python and other programming languages and apply machine learning concepts within six weeks. And to prove that we've got a specific timeline set out for a specific path with a timeline attached to it. So we're going to go through it now and it will show you exactly what you need to study and in what order and how much time you need to spend on each topic in order to get there. You're ready. Let's dive straight into it. All right. So you start here on the day and the first thing that we suggest looking at is statistics and all of these topics that we're going to cover of here. There's going to be 10 steps to get to the finish. We're going to talk about them in more detail in this section of the course, and you will get to know them a bit better. So don't worry, if you feel a bit like you don't really understand why something is important or why something is necessary, what it constitutes, that's OK. Because, first of all, we'll talk about it in this section of the course. And then moreover, we will cover off this whole map again in the conclusion of the course. Just to reiterate everything, once you have a different perspective, once you know what these topics are about. So statistics, we suggest spending six days on statistics and the reason. So we're going to mention in this story, we're going to mention the why the reason why we think these things are important and the reason why for statistics is to avoid costly mistakes. I'm a firm believer that you don't really need to know all the statistics in the world to be a successful data scientist. But you do need a core foundation in order to avoid mistakes and in order to avoid misrepresenting results and misinterpreting insights. And in this course, we'll tell you exactly what statistics to focus on. Its should be sufficient for you to be able to grasp those concepts in six days. Next step is to learn the data science process. And don't worry, we've got you covered here. We will actually walk through the data science process in one of the tutorials in this course. So that part we've got covered for you and you can always reference back to it. If you need a refresher. It's got five steps and the data science process is the process that pretty much any data science project goes through. And it's important to distinguish what we are talking about. Here is your roadmap to becoming a data scientist, whereas the data science process is the process. A data science project goes through and contains five steps and it's good to be aware of it so that you know the bigger picture. But once again, we've got you covered on this. And so let's proceed further. The next step is visualization and learning about visualization and picking a tool and learning how to apply the tool to create successful and insightful visualization. It should be sufficient. Ten days should be sufficient to get you going on the way. There are some very powerful and yet simple to use tools that allow a drag and drag and drop interface. Some examples of tableau, click, view and power by. So those are just three tools I can name off the top of my head that it takes like two or three days to get your head around and be able to use. And in ten days you can create. By then you can create like five very powerful visualizations and you will know sixty percent of that tool. You will know how to use it in how to leverage its power for your work. So we're allocating ten days to it so that you can get to get to know it and actually practice. In that time, we actually expect that you fulfill the next checkpoint. So these orange or these brown boxes identify check points and we expect that or we suggest in those 10 days you attempt your first data science project. So what we're saying here is that those ten days are enough to learn visualisation and practice some follow along kind of projects with instructors, whether you're taking these courses online in person, reading books or watching videos. But what we're saying with the checkpoint is like try to attempt your first data science project using visualization techniques that you learned in those ten days. And you. We see that it is actually not as difficult as you think, you might not be able to complete the project and to end. Not yet, but you will already be able to get some insights and get some of our information from the data next from there. The next step is database's. So the reason, the why behind databases is so that you expand your reach of data so that you can have access to more data. So up until this point, you're probably looking at data. Well, in this process or in statistics annualization, you will be probably looking at data in CSV files or Excel files, whereas if you learn the basics of Ezekial databases, you'll be able to get more data, access to more data and therefore perform more analytics. And again, we are looking five days to this simply because to get the basics of working with databases, five days is well more than enough to understand simple concepts like a select statement in a school that takes just a couple of days. And in five days you should be able to, you know, the basics of how to extract some data from a database. The next step in our path is statistical learning. And so this is the tutorial in this section of the course where you will get to meet at lunch. And if you haven't met him yet, then for the first time I'd the the is the second instructor on the course and we've created lots and lots of courses together. And so he'll be telling you about statistical learning. The reason why we're allocating only two days to statistical learning is not because it's a simple concept, but because we want to make sure that you don't get bogged down as the learning or you don't get too carried away with it. Those are two extremes that are not that are going to create hurdles for you. Progressing to becoming a data scientist in order to be a data scientist, you don't really need to know all the ins and outs of every single algorithm that you might need if you want to do research in that space of their sites. But some some basic statistical learning principles are going to be sufficient in order to just give you an example of what it's all about. So the the Y here is in order to get a feel for what's a typical learning is and to see that if you want to delve deeper into some machine learning algorithm, you can because of statistical learning. And then finally, next step. And probably the largest step in our journey is machine learning, how we're allocating 12 days to this because understanding how to apply machine learning algorithms and when to apply which one is very important. It is one of the most used concepts. In fact, it is the most used concept in the whole data science. You will see that a lot of business problems, a lot of industry problems, a lot of health problems or all sorts of those types of problems are actually solved using machine learning. And that's why it's such an important concept and such important space. And the idea here is that just take those 12 days to learn several other algorithms between three and five different core machine learning algorithms that you would look into something from regression, something from classification, something from clustering. Those are just branches of machine learning. Each one of them has like at least three to seven different algorithms. So but if you learn at least one algorithm from every single one of those branches, one from regression, one from clustering, one classification and maybe one or two other ones, just get to practice them. If you spend two days per algorithm, that should be totally enough to give you an understanding of, first of all, that you can do it. And second, the types of problems that they solve so that in the future, when you come across a problem, you know, I've seen this somewhere or it feels like I need to use this algorithm and I'm going to go do that. So that's machine learning. After that, we've got another check point attempt and advanced data science project. In fact, that checkpoint is actually included in those twelve days. So as you spend the 12 days learning three to five different algorithms, give it a shot to take on an advanced datasets challenge and you'll be surprised at how far you can progress already, then we will then we allocate five days to learning more about Python. So in this case, we're suggesting to apply machine learning using Python and therefore learning more about Python is going to help you speed up your process. So the how behind the learning or the why behind learning Python is to increase the agility with which you can solve these machine learning challenges. Five days should be enough to learn the basic principles of Python and get enough hands on practice so that you are more comfortable using Python. And finally, there are journey. We're allocating two days to deep learning. Again, it's not because it's a simple concept. It's rather to get a feel for deep learning to understand what is possible in the world of deep learning. And other opportunities, it opens up, so you always know that there's this alternative to your visual visual analytics, machine learning analytics, there's something even more advanced, which is called deep learning. And then one day when you do decide to dive into it, you know that it's there and you know where approximately in which direction you need to go. So it's a good good to have that overview. And so after that, there are 42 days you made it. If you follow this process and you diligently put in the hours every single one of these days that we specified here that at the end by day, 40 to you will be ready to go out there and to tell the world that a new data scientist has arrived and you can go on Kaggle and start looking at different competitions where you can participate. You can go on up work and create your data science profile and say that you're studying out data scientist and start bidding for jobs and get a hands on experience that way. And maybe if you're starting out, you're on your own, then you can approach your first client. It might be scary, but based on all the work you've done and all the hours that you've put in by this stage, you are ready to start into the real world. So that's that's our journey. And in fact, in this course, we've got two extra features for you. We'll give you a feel for what it's like to be a data scientist, will give you a feel for what it's like to apply visualization. We'll be talking about visualization for data mining. So we have a whole section on where you can get to play around with Tableau and solve a real world challenge step by step together with us. And also same thing for machine learning. We have a whole section where you'll actually solve two challenges together with us in machine learning using Python. So very excited about this course. We hope you're excited about this journey. As excited as we are, we've just come up with this concept and we were just introducing it to the world. So you are going to be our first subjects that are going to go through this. And we're very confident that if you follow this rigorously, if you take this course to the end and you actually get get a feel for what this is all about and you decide that it is what you want, it is what you're after, then then if you follow this map and you put in the hours and all of these days and you use credible sources for your knowledge, for your practice, then we're very confident that by day 40 to you'll be out there and ready to take on data science challenges. All right. So hope you're excited. Let's dive straight into the next Atauro and I'll see you there. Until then, enjoy data science.