Let's examine the various options and the most compelling scenarios for each one. By providing many different options for R developers to run their code in Azure, the company is enabling data scientists to extend their data science workloads into the cloud when tackling large-scale projects. Microsoft has fully embraced the R programming language as a first-class tool for data scientists. This article provides an overview of the various ways that data scientists can use their existing skills with the R programming language in Azure. The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.Many data scientists dealing with ever-increasing volumes of data are looking for ways to harness the power of cloud computing for their analyses.
We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. Rather than covering every R skill you might need, you’ll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data. We’ll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You will learn the R skills needed to answer essential questions about differences in crime across the different states. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States.
The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming.
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