Data Science technologies have transformed the way analysts used to mine the data. R programming language is one such leading data science technology. According to 2017 Burtch Works Survey 40% of surveyed data scientists prefer R. It is undoubtedly the most popular language used by the data scientists around the world.

R is a software environment and a programming language that was specifically created for statistical computations and graphical applications. It has a gained a lot of traction since its inception, now becoming the leading tool for machine learning, data analysis & visualization and statistics. With major boom in big data, a lot of data science job opportunities are getting created every day and expertise in R programming will assist you in taking your career as data scientist to the next level.

When you search for R programming course or class, you will find a lot of resources. But there are a few good quality courses that are available for free. Our team of experts has handpicked best quality R programming certification, courses, classes, tutorials and trainings available online. This list includes both free and paid courses that can be taken by any beginner and expert level learners. These are not only applicable for learning Data Science and Machine Learning but equally useful for anybody learning R programming for use in graphical and statistical fields.

Table of Contents

## 1. R Programming Course A-Z™: R For Data Science With Real Exercises (Udemy)

This is one of the best R programming tutorials that help you master programming in R and R studio with live examples. You will learn data analytics, data science, statistical analysis, packages, functions, GGPlot2.

More than 96,000 students have taken this Udemy R course. The course does not assume any prior knowledge or experience. It is designed in such a way that you can succeed at it even without any statistical background. It takes you step-by-step through the steep learning curve of R. You will be using specifically designed datasets to practice the skills you learn in the course.

The course begins with teaching R programming basics and how to combine programming and statistical concepts. Then the course progresses to advanced topics like matrices and data frames. All the course material is intertwined with ample of theory and real life examples to support learning. You will learn a new valuable skill in every single tutorial and in every section you will understand how you can apply that skill to solve real world problems.

**Key Highlights**

- Create visualizations to best capture your analysis and captivate your audience
- Learn to solve real life analytical challenges
- Learn how to customize R studio to suit your preferences
- Learn how to create and use vectors and matrices in R
- Learn how to install packages in R
- Practice working with financial, statistical and sports data in R
- Know all about Normal distribution and Law of Large Numbers
- Homework exercises for extra practice

**Duration : 10.5 hours on-demand video**

**Rating : 4.6**

## 2. Data Science and Machine Learning Bootcamp with R (Udemy)

In this R language course you will learn how to program with R, how to use R for data analysis, how to create amazing data visualizations, and how to use Machine Learning with R. The course has been developed and is taught by Jose Portilla who is one of the best instructors on Udemy and has taught thousands of students about Data Science and Programming. The program is designed to be suitable for both experienced professionals who want to change career track to data science and complete beginners who wish to learn data science and machine learning from ground up.

This is a very comprehensive R course with over 100 HD video lectures, detailed code notebooks for every lecture, 8 articles and 3 downloadable resources. It takes you through environment setup to begin with and then the basics of programming in R along with vectors, matrices and data frames. Then it covers data visualizations in R leading to data Capstone project. Further, the course delves into machine learning with a dozen of portfolio projects. You get a certificate of completion on finishing the course.

**Key Highlights**

- Create Data Visualizations
- Use R to manipulate data easily
- Use R to handle csv, excel, SQL files or web scraping
- Learn machine learning algorithms including topics like Linear regression, Logistic regression and more advanced topics such as decision tress, random forests and support vector machines
- Variety of R programming exercises, capstone projects and Machine Learning portfolio projects
- Access to online Q&A forum
- Explore data mining of Twitter for tending topics and creating a word cloud of these topics

**Duration : 17.5 hours on-demand video**

**Rating : 4.6**

## 3. R Programming Certification from Johns Hopkins University (Coursera)

This course is a part of Data Science Specialization from Johns Hopkins University. It is aimed at teaching R as a programming language and how to use R for effective data analysis. It covers practical issues in programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

This Certification in R programming starts with basic building blocks of R like datatypes, functions to read and write data etc. Then it proceeds to formulate how to write R programs using control structures, R functions and basic operations on data. You will also learn about code profiling and debugging. The course also explores how to simulate data in R, which serves as the basis for doing simulation studies.

The course is instructed by Roger D. Peng, PhD, Associate Professor, Biostatistics; Jeff Leek, PhD, Associate Professor, Biostatistics and Brian Caffo, PhD Professor, Biostatistics.

**Key Highlights**

- Learn how to install and configure software necessary for a statistical programming environment
- Cover the history of R and S
- Learn to collect detailed information using R profiler
- Understand programming language concepts and their implementation in R
- Make use of R loop functions and debugging tools
- Comes from highly reputed university and highly acclaimed professors

**Duration : Approx. 20 hours**

**Rating : 4.6**

## 4. Programming for Data Science with R Nanodegree Program (Udacity)

This Nanodegree program prepares learners for a career in Data science by teaching them the fundamental data programming tools of R, SQL, command line and git. The is an introductory program and is structured as a series of 3 courses – Introduction to SQL, Introduction to R Programming and Introduction to Version Control. During the course of the program, learners complete three projects, with a focus on the R language.

In the module on R programming, you will start by understanding common use cases of R and why it’s popular along with installation & setup of R Environment. You will learn to represent and store data using R data types and variables, and use conditionals and loops to control the flow of programs. You’ll also learn about complex data structures like lists to store collections of related data. Additionally you’ll learn to write your own custom functions, write scripts, and handle errors. Data visualization using R libraries is also covered in depth.

**Key Highlights**

- Learn the most important programming languages (R and SQL) used by the data scientists
- Learn to make beautiful visualizations using the ggplot2 library
- Use the popular diamonds dataset to put your R skills to work
- Industry relevant projects to gain hands-on experience
- Personalized feedback on projects from network of 900+ project reviewers
- Get access to student hub to connect with fellow learners
- Get access to technical mentor support and career support services
- No prior experience requirement to enrol for the program

**Duration : 3 months, 10 hours per week**

**Rating : 4.5**

## 5. Data Science: R Basics Certificate by Harvard University (edX)

This is the first course in the 9-part Data Science professional certificate program offered by HarvardX on the edX platform. The goal of this edX R course is to introduce learners to the basics of R programming. This course has no prerequisites so it is suitable for anybody making a start in Data Science field. It is equally useful for anyone who has programming experience in another language, but would like to learn R programming.

This R programming certification course uses a real-world dataset about Crime in the United States to teach how to solve real problems using R. It covers R’s functions and data types, vector operations and advanced functions like sorting. You’ll learn how to apply general programming features like conditional construct “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.

The course instructor Rafael Irizarry does a great job explaining topics in plain language making even complex topics very easy to understand. There are several programming assignments to solidify the learning. The course is available for free but you need to pay a small fee for graded exams and certificate of completion of course.

**Key Highlights**

- Build a strong foundation to prepare for more in-depth courses
- Learn 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
- Learn to perform operations in R including sorting and making plots
- Learn to solve problems using real life dataset

**Duration : 8 weeks, 1 to 2 hours per week**

**Rating : 4.6**

## 6. Statistics with R Certification by Duke University (Coursera)

Statistics with R certification is one of the best courses to master statistics with R. You will learn to analyze and visualize data in R and create reproducible data analysis reports.

This R statistics specialization includes 5 courses – Introduction to Probability and Data, Inferential Statistics, Linear Regression and Modeling, Bayesian Statistics and Statistics with R Capstone. The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. The dataset for analysis will be provided to learners and they will need to apply various methods and techniques learnt in the previous courses.

Dr. Mine Çetinkaya-Rundel is the main instructor of the program along with three other Professors from the Duke University’s Department of Statistical Science. No programming experience is needed to take this program, just knowledge of basic maths and a genuine interest in data analysis.

**Key Highlights**

- Gain statistical mastery of data analysis including inference, modelling and Bayesian approaches
- Learn to wrangle and visualize data with R packages for data analysis
- Understand simple and multiple linear regression models
- Perform frequentist and Bayesian statistical inference and modeling to make data-based decisions
- Gain expertise needed to apply for statistical analysis or data scientist positions
- Plenty of practice exercises and tests
- Access to forum with great help to solve doubts

**Duration : Approx. 7 months, 4 hours per week**

**Rating : 4.7**

## 7. R Programming: Advanced Analytics In R For Data Science (Udemy)

If you have basic knowledge of R programming language and want to take your skills to the next level, then this is the best R programming course for you. It focuses on data science & analytics and statistical analysis in R language. The instructor Kirill Eremenko takes you through the complex concepts in quite simplified and easy to understand manner.

The course consists of 51 lectures that cover Data preparation, Lists in R and “Apply” family of functions in detail. You will learn how to prepare data for analysis in R, perform median imputation method, work with date-times in R, how to use lists in R, how to use apply functions instead of loops, how to nest user defined functions with apply-type functions etc.

This course is not for complete beginners, and assumes basic knowledge of R. Knowledge of GGPlot2 package, dataframes, vectors and vectorized operations is also recommended.

**Key Highlights**

- Advanced level course for those who want to dive deep in R
- Professional R Video training
- Unique datasets designed with years of industry experience in mind
- Learn to create a timeseries plot in R
- Understand how the Apply family of functions works
- Locate missing data in your dataframes
- Learn to apply Factual Analysis method, Median Imputation method to replace missing records
- Engaging exercises to help correlate analytics in the real world

**Duration : 6 hours on-demand video**

**Rating : 4.7**

## 8. Mastering Software Development in R Certification by Johns Hopkins University (Coursera)

This specialization in R programming provides rigorous training in R language and also teaches the best software development practices for building data science tools that are not only robust, modular and reusable but also collaborative (thus suitable for use in team based and community environments). Through this program you will gain necessary skills for handling complex data, building R packages and developing custom data visualizations.

This R language certification program comprises of 5 courses. It starts with an introduction to R (essential foundational concepts of R) and then moves on to advanced topics like functional programming, object-oriented programming, error-handling, user-functions, R packages and software maintenance. It ends with a Capstone project in R programming. You will find that throughout the program, the focus is on aspects of R language that are useful for creating tools and code that can be used by others.

The course assumes some programming experience (in any language) and working knowledge of mathematics up to algebra.

**Key Highlights**

- Gain fluency at the R console
- Be able to create tidy datasets from a wide range of possible data sources
- Learn to define new data types in R and develop a universe of functionality specific to those data types
- Learn how to distribute packages via CRAN and GitHub
- Create new visualization building blocks using the ggplot2 framework

**Duration : Flexible**

**Rating : 4.4**

## 9. Learning R (LinkedIn Learning – Lynda)

This is a beginner level course that introduces you to the R programming language. It starts with instructions on installation of R, setting up the R environment and using R Studio. Then it proceeds to cover how to read data from spreadsheets & SPSS, how to use and manage packages for advanced R functions.

The instructor of this course is Barton Poulson who is a professor, designer and data analytics expert. He effectively takes you through several examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools.

**Key Highlights**

- Learn to use charts, such as histograms, bar charts, scatter plots, and box plots, to get the big picture of your data
- Learn descriptive statistics such as means, standard deviations, and correlations for a more precise depiction
- Learn inferential statistics like regression, t-tests, the analysis of variance, and the chi-square test to determine the reliability of your results
- Learn to create beautiful presentation charts to share your analysis results
- Several engaging exercises available with data sets which can be downloaded
- View Offline mode allows learners to download courses on mobile devices and watch them on the go without an internet connection

**Duration : 2 hours 25 minutes**

**Rating : 4.6**

## 10. R Programming for Statistics and Data Science (Udemy)

This R Certification program is the most comprehensive introduction to R programming for Statistics and Data Science. The aim of this course is to progress you from being a complete beginner in R language to an expert professional who can take up data manipulation on demand. You will be exposed to fundamentals of programming, data manipulation techniques and tools, and data visualizations and plots. It also includes a step-by-step guide to statistics.

This R programming class is a very good blend of theory and practice. It takes care to incrementally build your theoretical knowledge and practical skills. Multiple exercises included in the course help to reinforce your learning. It also includes homework and projects to further challenge the learners. On completion of the course, you will receive a verifiable certificate.

**Key Highlights**

- Learn descriptive statistics and fundamentals of inferential statistics
- Master confidence intervals and hypothesis testing, as well as regression and cluster analysis
- Learn to work with vectors, matrices, data frames, and lists
- Become adept in ‘the Tidyverse package’ enabling you to index and subset data
- Learn the grammar of graphics and the ggplot2 package
- Learn how to visualise data – plot different types of data & draw insights
- Learn complete skill set to tackle a new Data Science project
- Learn to make decisions that are supported by the data

**Duration : 6.5 hours on-demand video**

**Rating : 4.5**

## 11. Statistics with R – Beginner Level (Udemy)

This course on Udemy focuses on basic statistical analyses using R programming language. You will learn how to manipulate data in R and prepare it for the analysis – filtering your data frame, recoding variables and computing new variables. Also learn about computing the main statistical figures in R – mean, median, standard deviation, skewness, kurtosis etc., and data visualization using tables and charts.

The course includes 3 hours on-demand video, 13 articles and 12 downloadable resources and comes with full lifetime access. Though this is a brief course, it covers essential concepts thoroughly with examples for illustration. Large variety of data frames are provided for practice, along with notes and R files. This is a beginner level course and assumes knowledge of basic statistics.

**Key Highlights**

- Learn to Build frequency tables and cross tables
- Learn to Create histograms and cumulative frequency charts
- Learn to Build column charts, mean plot charts, boxplot charts and scatterplot charts
- Get statistical indicators by subgroups of the population
- Learn how to check for normality and for the presence of outliers
- Learn to determine skewness and kurtosis
- Learn to perform one-sample t test, the binomial test and the chi-square test for goodness-of-fit

**Duration : 3 hours on-demand video**

**Rating : 4.2**