 Personally, I think data curation should be required knowledge for all data scientists, but honestly, most of us don't know what it is or how it can help us. Data curation files consist of all the files associated with documenting data for your project. These include flowcharts, explanatory tables, diagrams, cheat sheets, manuals, data dictionaries, survey documentation, data warehouse documentation, and policies and procedures associated with the data. So if that sounds like a lot of boring paperwork, let me convince you, it's totally not. You actually get to do some really fun visualizations. Let me show you. So making dashboards is hot, hot, hot in data science. But do you know how to design one? You have to mock it up first, which you can do in PowerPoint. This is from a real project I did. Mocking up dashboards is data curation. In data curation, you also make diagrams about your data. Here you will see an example of a data reduction diagram, which shows how data were removed from a big data set to pare it down to a smaller data set. Okay, now here's the serious business side of data curation. Sometimes your data don't come out as planned. Here's a diagram I did for a customer who had a lot of unknowns in the question on her survey data that she didn't expect to see. So I had to make a series of curation files to troubleshoot what went wrong so we could figure out what to do about it. This brings me to the main reason to learn how to do data curation. Even though I love the sexy visualizations, the main reason I think all data scientists should learn data curation is a totally improved communication about data on your team. These are stressful enough at work already, so there's no need to also be fighting as a result of data confusion. Data curation addresses that problem of chronic miscommunication about data. I'm Monica Wahee, an author on LinkedIn Learning. If you want to learn my tips and tricks in data curation, take my course, Data Curation Foundations. The link is in the description.