 The last thing I want to say in data science and introduction where I'm trying to define data science is to talk about teams in data science. The idea here is that data science has many different tools and different people are going to be experts in each one of them. Now, you have for instance, coding and you have statistics. Also, you have fields like design or business and management that are involved. And the question of course is, who can do all of it? Who's able to do all of these things at the level that we need? Well, that's where we get this saying I've mentioned it before, it's the unicorn. And just like in ancient history, the unicorn is a mythical creature with magical abilities. In data science, it works a little differently. It is a mythical data scientist with universal abilities. The trouble is, as we know from the real world, there's really no unicorns, animals, and there's really not many unicorns in data science. Really, there's just people. And so we have to find out how we can do the projects even though we don't have this one person who can do everything for everybody. So let's take a hypothetical case just for a moment, I'm going to give you some fictional people. Here is my fictional person auto, who has strong visualization skills, who has good coding, but has limited analytics or statistical ability. And if we graph his stuff out his ability, so here we got five things that we need to have happen. And for the project to work, they all have to happen at at least a level of eight on the zero to 10. If we take his coding ability, he's almost there, statistics, not quite halfway, graphics, yes, he can do that. And then business, all right, and project pretty good. So what you can see here is in only one of these five areas is auto sufficient on his own. On the other hand, let's pair him up with somebody else, let's take a look at Lucy. And Lucy has strong business training has good tech skills, but has limited graphics. And so if we get her profile on the same thing that we saw, there's coding, pretty good statistics, pretty good graphics, not so much business, good, and projects. Okay, now the important thing here is that we can make a team. So let's take our two fictional people auto and Lucy, and we can put together their abilities. Now actually have to change the scale here a little bit to accommodate the both of them. But our criterion still is at eight, we need a level of eight in order to do the project competently. And if we combine them, oh, look, coding is now past eight, statistics is past eight, graphics is way past, business way past, and then the projects there too. And so when we combine their skills, we are able to get the level that we need for everything, or to put it another way, we have now created a unicorn by team. And that makes it possible to do the data science project. So in sum, you usually can't do data science on your own, that's a very rare individual, or more specifically, people need people. And in data science, you have the opportunity to take several people and make collective unicorns so you can get the insight that you need in your project, and you can get the things done that you want.