 Data science is a field that is strongly associated with its methods or procedures. In this section of videos, we're going to provide a brief overview of the methods that are used in data science. Now, just as a quick warning, in this section, things can get kind of technical and that can cause some people to sort of freak out. But this course is a non technical overview. The technical hands on stuff is in the other courses. And it's really important to remember that tech is simply the means to doing data science. insight or the ability to find meaning in your data, that's the goal tech only helps you get there. And so we want to focus primarily on insight and the tools and the tech as they serve to further that goal. Now there's a few general categories we're going to talk about again, with an overview for each of these. The first one is sourcing or data sourcing, that is, how to get the data that goes into data science, the raw materials that you need. The second is coding that again is computer programming that can be used to obtain and manipulate and analyze the data. After that, a tiny bit of math, that is the mathematics behind data science methods that really form the foundations of the procedures. And then stats, these statistical methods that are frequently used to summarize and analyze data, especially as applied to data science. And then there's machine learning ML. This is a collection of methods for finding clusters in the data for predicting categories or scores on interesting outcomes. And even across these five things, even then, the presentations aren't too techy crunchy, they're basically still friendly. And you know, really, that's the way it is. And so that is the overview of the overviews. In sum, we need to remember that data science includes tech, but data science is greater than tech, it's more than those procedures. And above all, that tech well important to data science is still simply a means to insight in data.