This is the Getting and Cleaning Data course overview. This is actually one of the more unique classes in our data science track, but I think its sort of one of the most fundamental components of being a data scientist. Which is being able to go out and get data from whatever source it's in and whatever form it's in, and turning it into a clean processed data set, that then you can use to answer questions. So in this class, we'll be talking about raw versus tidy data, how to download files. Reading in data from a very large number of different sources, merging it together, reshaping it, summarizing it, and then finding some data resources that you can use to augment the data that you already have. So here are a couple of different things that you might learn about. So, for example, how to connect to a MySQL database, a MySQL database, from R. So this is actually using an R package to go to R MySQL, and you'll be able to connect the databases and read data from that. Another idea would be sort of merging data together. So you take different components of a data sets. So there might be one cloud that contains reviews and one cloud that ca, contains solutions, say from a peer assessment, and you want to combine them together. And so, you can use commands in R to merge those data sets together. And then, talking about, sort of, raw versus processed data. So talking about, what are the data that come to you in their rawest possible form, the original source of the data versus the processed data. The data that's ready for analysis, ready to be used by people that has happened after you've merged it, and sort, subsetted it, and transformed it into the nice tidy data set that people can use. So that's getting data.