- Data Science: Foundations using R: Johns Hopkins University
- Data Analysis with R: Duke University
- Google Data Analytics: Google
- Data Analysis with R Programming: Google
- R Programming: Johns Hopkins University
- Getting Started with R: Coursera Project Network
- Data Visualization & Dashboarding with R: Johns Hopkins University
- Introducción a Data Science: Programación Estadística con R: Universidad Nacional Autónoma de México
- Statistical Analysis with R for Public Health: Imperial College London
- Expressway to Data Science: R Programming and Tidyverse: University of Colorado Boulder

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. While R is widely used among statisticians and data miners for statistical software and data analysis, it is becoming popular among data scientists as well. Proficiency in R has become a standard requirement for many data science and statistician-related jobs.

We recommend these courses for those with some basic knowledge of R that want to use it in a Data Science and Software Development context. You may wish to use it for exploratory data analysis, data visualization, probability, inference, linear regression, and more. You may desire to expand your data science skills and use R in your everyday role to interact more with the data you work with.

According to Burning Glass, there were ~50K job postings in the last 12 months requiring R as a skill. Some of the functional job titles requiring this skill are Business Intelligence Analyst, Operations Research Analyst, Management Analyst, and Market Research Analyst. If your work involves data analysis, modeling, or visualization, R is a powerful skill to add to your portfolio.

While there are generally no specific prerequisites for beginner-level R courses, programming language basics is one of the key topics you typically need to know before you start to learn R programming language. It can also help if you have a solid understanding of math and statistics before beginning R courses. Introductory-level data visualization skills, such as understanding how plots and graphs work, can also be helpful when learning the R programming language. Additionally, analytical and critical thinking skills can be helpful when you’re starting to study R.

People who are highly analytical and curious are typically best suited for roles in R. People who enjoy statistics and using statistical models to solve problems can also be well suited to roles in R. Those who have an affinity for using computer technology and machine learning can potentially be well suited to learning this programming language. People who enjoy highly detailed tasks and who have strong problem-solving skills are typically well suited for R roles. Additionally, people who work in fields like lean manufacturing, public health, and finance can be well suited for roles in R.

Some common career paths for people in R include machine learning engineers, data scientists, data miners, data engineers, and statisticians. These fall under the category of computer programming jobs, which according to the Bureau of Labor Statistics pay a higher than average wage. These types of computer programming jobs typically require a bachelor’s degree. Competition can be stiff for these jobs, though, due to the influx of workers in the field as a result of global trade.

Mathematics, statistics, and AI are topics you could study that are related to R. Data visualization is another related topic, along with probability and the broader field of data science. You could study the related topics of data structure, data analysis, and data type as part of your R studies. Data manipulation and data cleansing are related topics that you could study, and you could also study GitHub and regular expression (REGEX).

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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