 We'll continue our definition of data science by looking at the roles that are involved in data science, the way that different people can contribute to it. That's because it tends to be a collaborative thing. And it's nice to be able to say that we're all together working together towards a single goal. So let's talk about some of the roles involved in data science, and how they contribute to the projects. First off, let's take a look at engineers. These are people who focus on the back end hardware, for instance, the servers, and the software that runs them. This is what makes data science possible. And it includes people like developers, software developers, or database administrators. And they provide the foundation for the rest of the work. Next, you can also have people who are big data specialists. These are people who focus on computer science and mathematics. And they may do machine learning algorithms as a way of processing very large amounts of data. And they often create what are called data products. So a thing that tells you what restaurant to go to or that says you might know these friends or provides ways of linking up photos, those are data products. And those often involve a huge amount of very technical work behind them. There are also researchers. These are people who focus on domain specific research. So for instance, physics or genetics or whatever. And these people tend to have very strong statistics. And they can use some of the procedures and some of the data that comes from the other people like the big data researchers. But they focus on the specific questions. Also, in the data science realm, you'll find analysts, these are people who focus on the day to day tasks of running a business. So for instance, they might do web analytics like Google Analytics, or they might pull data from a SQL database. And this information is very important and good for business. And so analysts are key to the day to day functioning of business. But you know, they may not exactly be data science proper, because most of the data they're working with is going to be pretty structured. Nevertheless, they play a critical role in business in general. And then speaking of business, you have the actual business people, the men and women who organize and run businesses. These people need to be able to frame business relevant questions that can be answered with the data. Also, the business person manages the project and the efforts and the resources of others. And while they may not actually be doing the coding, they must speak data, they must know how the data works, what it can answer, and how to implement it. You can also have entrepreneurs. So you might have, for instance, a data startup, they're starting their own little social network of their own little web search platform. An entrepreneur needs data and business skills. And truthfully, they have to be creative at every step along the way, usually because they're doing it all themselves at a smaller scale. Then we have in data science something known as the full stack unicorn. And this is a person who can do everything at an expert level. And they're called a unicorn because truthfully, they may not actually exist. I'll have more to say about that later. But for right now, we can sum up what we got out of this video by three things. Number one, data science is diverse. There's a lot of different people who go into it. And they have different goals for their work, and they bring in different skills and different experiences and different approaches. Also, they tend to work in very different contexts. An entrepreneur works in a very different place from a business manager works in a very different place from an academic researcher. But all of them are connected in some way to data science and make it a richer field.