Hi everyone, welcome to Reproducible Research. This class is, is the fifth class in the Data Sci Specialization. And it covers a very special topic that I think doesn't get talked about a lot. In kind of, in typical, typical statistical or data analysis types of courses. So even though the word reproducible research has the word research in it, it's, so we're not, it, it applies to many different areas. Not just to kind of people who are doing research per say. The basic idea is that when you do a data analysis and there are many different steps, and it's, and it involves a lot of computing, and perhaps a lot of kind of of data manipulation or processing. It's important that you, when you communicate what you've done and you've communicated in a way that someone else could actually reconstruct what you've done. So that's reproducible by someone else. Now I think it's ve-, as data analyses and data sets get more complicated, it becomes harder and harder to insure that whatever you've done is actually reproducible. Because sometimes, you lose a little code over here. Sometimes you forget about a, a process or a transformation you did over here. And then, if you, and then a subsequent analyses are not reproducible if you don't kind of document all those details. So what were going to talk about in this course are kind of some basic tools that you can use to help make your analyses and your work in general reproducible. And I'll talk about thing like Knitter and we'll talk about how we can do this in RStudio. And we'll talk about some basic principles for kind of that you can follow to make sure that you're work is as reproducible as possible. So I think this a very important aspect of any data analysis in any area. because it's really about communicating exactly what you've done so that someone else can kind of understand what's going on. In the last part of the course, we'll talk about some case studies. Of kind of where reproducibility either went right or went horribly wrong. And I think these are, these cases studies are, are highly instructive. And to kind of give you a sense of what can and can what is and is not possible. So I hope you enjoy this course. I think it's a really important idea and I hope you get a lot out of it.