Get a taste of big data and the R programming language, an open-source language developed specifically for exploring and modeling data. Explore some of the courses available and the advantages of R.
(upbeat guitar music) - [Narrator] Every element of our professional lives and even our personal lives is being transformed by data. How you find jobs, how you find clients, how you organize your time and choose projects, how you decide what has greatest value and the most potential for helping you achieve your goals. All of these are different now because of the ready availability of data. But if you want to get the benefits of this data revolution, then you need to know how to work with data, and one of the best ways ways to do that is with R, a free and an open source language that was specifically developed for exploring and modeling data to help you find the insight that you need. - [Narrator] Many people think of R as statistical software. But it's fundamentally not, and it's a little bit upsetting when people say that. R is a programming language that has been adopted and curated by people interested in doing data science, as flexibly as possible, and without having to think about the actual programming side of things, what the computer's doing behind the scenes. That's what makes it a really great scripting language to use for data science. - [Narrator] Your job in wrangling data is to develop an understanding of your unique data sets, to figure out how they're messy in their own ways. You can then use data manipulation tools in R to properly structure your data as tidy data. Once you've done that, a whole world of data analysis tools becomes available to you. Tidy data unlocks a set of tools known as the tidyverse. The tidyverse consists of a set of R packages that work together to transform, analyze, and visualize tidy data. The tools of the tidyverse can easily share data with each other, and allow you to quickly take advantage of the power of R for your analysis. - [Narrator] I used the tidyverse to rapidly explore, understand and analyze research data sets, with a fully reproducible work flow. I'll show you how the tidyverse makes R into a sharp, efficient and easy to use tool, for importing data from CSV files, Excel files and foreign formats like Starter and SPSS, for exploratory data analysis visualizations, to get a feel for your data, and for quantitatively understanding your data. - [Narrator] When you're exploring data, quick is better. You want to take a quick snapshot of data, just to kind of get a feel for what this stuff looks like, and R provides the command stem to produce stem and leaf plots which are for exactly this purpose. Stem is a really quick and easy way to explore the contents of a vector, to find out if there's any sort of a relationship or any sort of a distribution that might be significant.