 So what we're trying to do is find that we can really help people get started with data science. So we've thought about all what are the five things that people need to know to get started with doing data science. And the first step is really understand the process. What's the entire data science process? How do you frame a problem? How do you acquire the data to solve that problem? How do you clean it up? How do you visually explore it? How do you transform it? Model it, build something. We kind of cover that as one of the first days to see end to end what happens. And then we go deeper into some of these arguments. One of the ones would be around visualization. So how do you, how can you visualize? How do you understand a grammar of visualization? Can you explore and visualize multi-dimensional data? Can you find the patterns and trends of this? Can you build an interactive dashboard? We then go and understand a little bit of math behind algorithms. So, you know, basics of linear algebra, statistics, and what goes inside these algorithms, right? Kind of unpack that. Unpack that really in a kind of a hacker way. So using code and visuals to understand them rather than, rather than mathematical because of that. So that's day three. Day four is then going deeper into the algorithm. So how do we pick algorithms? How do we tune algorithms? How do we select between different sets of algorithms? And really understand how this ML machine learning algorithm selection process works. And once we've done that, day five finally is really around, really around unpacking, taking what you've probably been doing and saying, I want to expose it as a service for a web application or a dashboard to consume. So how do we convert that into an EPI or a service? And how do I design an application that we consume? And potentially do it on the cloud, right? Because we want it to happen. So these five days will give you enough experience to really build, you know, understand the process, each of the important steps within it, the breaks in between the five weekends that we're doing the course and allow you to go back and apply this within your own project. So you also build over the course of the bootcamp, a personal portfolio, at least start the process of that. So at the end of the five weeks, you have a sufficient understanding of what's happening and also have sufficient exposure to a few other problems which you try to experiment on as you learn doing and learning in the middle of the class. So you get both a classroom experience, you also get an experience of building a few projects on your own. Once you finish this, you'd be kind of prepared to know what to do next. What should I do more of these data science projects? How do they come complete? How do we continue to build my profile? Or should I go deeper into some element of those data science to focus more on the recommendation system, focus much more on classification problems, focus much more on business problems, should I go to a building? So you'd be prepared to go further. You would start to build your portfolio. You'd be ready to start talking to people intelligently about where we can use the information building data science.