 The first thing we want to cover in statistics and data science is the principle of exploring data, and this video is just designed to give an exploration overview. So we like to think of this the intrepid explorers. They're out there exploring and seeing what's in the world. You can see what's in your data. More specifically, you want to see what your data set is like. You want to see if your assumptions are met so you can do a valid analysis with your chosen procedure. And really, something that may seem very weird is you want to listen to your data is something's not working out. If it's not going the way you want, then you need to pay a little more attention and exploratory data analysis is going to help you do that. Now there are two general approaches to this. First off, there's a graphical exploration. So you use graphs and pictures and visualizations to explore your data. The reason you want to do this is that graphics are very dense and information. They're also really good. In fact, the best way to get the overall impression of your data. Second to that, there is a numerical exploration. I make it very clear. This is the second step, do the visualization first, then do the numerical part. Now you want to do this because it can give greater precision. And this is also an opportunity to try variations on the data. You can actually do some transformations, move things around a little bit and try different methods and see how that affects the results and see how it looks. So let's go first to the graphical part. There are very quick and simple plots that you can do. Those include things like bar charts and histograms and scatter plots. Very easy to make and very quick way of getting the understanding of the variables in your data set. In terms of numerical analysis, again, after the graphical methods, you can do things like transform the data that is take like the logarithm of your numbers. You can do empirical estimates of population parameters. And you can use robust methods. And I'll talk about all of those in more length and later videos. But for right now, I can sum it up this way. The purpose of exploration is to help you get to know your data. And also, you want to explore your data thoroughly before you start modeling it before you build statistical models. And all the way through, you want to make sure you listen carefully so that you can find hidden or unassumed details and leads in your data.