![]() ![]() We've talked about its use in stats and in graphing. Yet another benefit is R's extensive functionality. Every aspect of R is free to use, unlike some other stats packages you may have heard of EG, SAS or SPSS. Additionally, as you can see in this graph, knowing R is one of the top five languages asked for in data scientist's job postings. This makes R a great language to learn as the more popular software is, the quicker new functionality is developed, the more powerful it becomes and the better this support there is. R is quickly becoming the standard language for statistical analysis. Outside of this course, you may be asking yourself, "Why should I use R?" One reason to want to use R it's popularity. While this might be your first brush with it, we will be returning to CRAN time and time again when we install packages, so keep an eye out. R is downloaded from the Comprehensive R Archive Network or CRAN. It will be one of the main tools you use in this and following courses. R is both a programming language in an environment focused mainly on statistical analysis and graphics. First, let's remind ourselves exactly what R is and why we might want to use it. ![]() ![]() Now that we've got a handle on what a data scientist is, how to find answers, and then spend some time going over data science example, it's time to get you set up to start exploring on your own. ![]()
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