 To wrap up our course, Statistics and Data Science, I want to give you a short conclusion and some next steps. Mostly I want to take a little piece of advice I learned from a professional saxophonist, Kirk Whalum, and he says, there's always something to work on. There's always something you can do to try things differently to get better. It works when practicing music. It also works when you're dealing with data. Now, there are additional courses here at datalab.cc that you might want to look at. There are conceptual courses, additional high-level overviews on things like machine learning, data visualization, and other topics, and I encourage you to take a look at those as well to round out your general understanding of the field. There are also, however, many practical courses. These are hands-on tutorials on the statistical procedures I've covered, and you learn how to do them in R and Python and SPSS and other programs. But whatever you're doing, keep this other little piece of advice from writers in mind, and that is right what you know. And I'm going to say it this way, explore and analyze and delve into what you know. Remember, when we talked about data science and the Venn diagram, we've talked about the coding and the stats, but don't forget this part here on the bottom. Domain expertise is just as important to good data science as the ability to work with computer coding and the ability to work with the numbers and quantitative skills. But all through it, remember this, you don't have to know everything. Your work doesn't have to be perfect. The most important thing is just get started. You'll be glad you did. Thanks for joining me, and good luck.