 As we work to get a grasp on data science, there's one more contrast I want to make explicitly, and that's between data science and business intelligence or BI. The idea here is that business intelligence is data in real life. It's very, very applied stuff. The purpose of BI is to get data on internal operations on market competitors and so on and make justifiable decisions as opposed to just sitting in the bar and doing whatever comes to your mind. Now, data science is involved with this, except, you know, really, there's no coding in BI. There's using apps that already exist. And the statistics in business intelligence tend to be very simple. They tend to be counts and percentages and ratios. And so it's simple. The light bulb is simple. It just does its one job. There's nothing super sophisticated there. Instead, the focus in business intelligence is on domain expertise and on really useful direct utility. It's simple, it's effective, and it provides insight. Now, one of the main associations with business intelligence is what are called dashboards or data dashboards. They look like this. It's a collection of charts and tables that go together to give you a very quick overview of what's going on your business. And while a lot of data scientists may, let's say, look down their nose upon dashboards, I'll say this. Most of them are very well designed and you can learn a huge amount about user interaction and the accessibility of information from dashboards. So really, where does data science come into this? What's the connection between data science and business intelligence? Well, data science can be useful to BI in terms of setting it up, identifying data sources and creating or setting up the framework for something like a dashboard or a business intelligence system. Also, data science can be used to extend it. Data science can help get past the easy questions and the easy data to get the questions that are actually most useful to you, even if they require really sometimes data that's hard to wrangle and work with. And also, there's an interesting interaction here that goes the other way. Data science practitioners can learn a lot about design from good business intelligence applications. So I strongly encourage anybody in data science to look at them carefully and see what they can learn in some business intelligence or BI is very goal oriented. Data science, perhaps prepares the data and sets up the form for business intelligence, but also data science can learn a lot about usability and accessibility from business intelligence. And so it's always worth taking a close look.