Reproducible research is another one of or more unique courses. So reproducible research is concerned with creating code and documents that will completely reproduce all of the analysis that you've done in a transparent way so that you can communicate that to other people. It's one of the most fundamental components of being a data scientist. But it's often glossed over as a sort of a side component of many other programs of data science. So we're going to talk about in the reproducible research course, the structure of a data analysis, how do you set it up, how do you organize it and put it together. We're going to talk about some of the components of reproducible research in terms of programming which include Markdown, LaTex, R Markdown. And then we're going to talk a little bit about a unique idea called evidence-based data analysis, doing data analysis based on what's best practices in the field right now. And then ways that you can publish your data outside of your own organization, things like RPubs. So for example, we're going to talk about the steps in a data analysis or in a data science problem. And so, you have to go all the way from defining the questions to creating sort of reproducible code that you can share with other people. And so, some of these steps are covered by different parts of the class, and so reproducible research synthesizes where those parts go together. We'll also talk about which files are components of a data analysis. Everything from the sort of a raw data to the exploratory figures to sort the final analysis that you'll be performing.