So you want to learn Data Science with R? **Good Decision!** Because R programming has various statistical and graphical capabilities. R has a huge variety of libraries to perform statistical analysis. Some most powerful visualization packages in R are **ggplot2, ggvis, googleVis, and rCharts.** So if you are looking for the **Best Online Courses for Data Science with R**, then this article will help you.

In this article, you will find the 10 Best Online Courses for Data Science with R programming. And these courses are filtered out on the following criteria-

**Criteria-**

- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.

So, without wasting your time, let’s start finding the Best Online Courses for Data Science with R-

**Best Online Courses for Data Science with R**

- 1. Data Science: Foundations using R Specialization– Coursera
- 2. Applied Data Science with R Specialization- Coursera
- 3. Programming for Data Science with R– Udacity
- 4. Data Scientist with R- Datacamp
- 5. Data Science Specialization– Coursera
- 6. Data Science and Machine Learning Bootcamp with R- Udemy
- 7. Data Visualization & Dashboarding with R Specialization- Coursera
- 8. Statistical Analysis with R for Public Health Specialization- Coursera
- 9. R for Data Science- Book
- 10. Statistics and Statistics with R Tutorials– MarinStatsLectures
- Conclusion

**1. ****Data Science: Foundations using R Specialization**– Coursera

**Data Science: Foundations using R Specialization**– Coursera

Rating- | 4.6/5 |

Provider- | Johns Hopkins University |

Time to Complete- | 5 months (If you spend 8 hours/week) |

This is a specialization program offered by **Johns Hopkins University**. There are **5 courses** in this program. In the first course, you will understand **what is data science, R programming basics, and GitHub and R Studio.**

The next course is a detailed lesson on **R Programming**, where you will learn how to program with R Programming. The next course is all about **cleaning the data. **In this course, you will learn how to **read various file types **and how to **manage Data Frames with dplyr.**

Course 4 will teach **Exploratory Data Analysis** and cover the Lattice system and the ggplot2 system. This course also has case studies on** Air Pollution and Clustering.** The last course of this specialization program will teach **Reproducible Research** and **knitr**. knitr is a literate programming tool.

This specialization program has** quizzes and exercises** throughout the program.

**Courses Details-**

**The Data Scientist’s Toolbox****R Programming****Getting and Cleaning Data****Exploratory Data Analysis****Reproducible Research**

**Extra Benefits-**

- You will earn
**Shareable Specialization and Course Certificates**. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

**Drawback-**

- The last course of this program is very basic and doesn’t cover the concept in detail.

**Who Should Enroll?**

- Those who have some programming experience in any language and have some working knowledge of mathematics up to algebra.

**Interested to Enroll?**

If yes, then check out the details here-

Data Science: Foundations using R Specialization

**2. Applied Data Science with R Specialization- Coursera**

Rating- | 4.3/5 |

Provider- | IBM |

Time to Complete- | 5 months (2 hours per week) |

This is a **new specialization program **offered by IBM for those who want to learn **Data Science with R programming**. In this specialization program, there are 5 courses. The first course covers **R programming fundamentals **and how to **work with Data using R programming.** Course 1 also has one project.

In the next course, you will learn the **SQL basics and some intermediate SQL concepts** such as how to use **String Patterns and Ranges, how to sort Result Sets, Built-in Database Functions, etc.**

The third course will teach the complete process of data analysis using R starting from **Data wrangling** to **Model Development** using R Programming.

Next, you will learn the **ggplot2 library** for performing data visualization in R Programming. In the end, there is one **Capstone Project**. In this project, you have to use all the concepts learned throughout the program. This project helps you to **test your understanding.**

**Courses Details-**

**Introduction to R Programming for Data Science****SQL for Data Science with R****Data Analysis with R****Data Visualization with R****Data Science with R – Capstone Project**

**Extra Benefits-**

- You will earn
**Shareable Specialization and Course Certificates**. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

**Drawback-**

- The first course is not for beginners because the instructor covers some advanced concepts.

**Who Should Enroll?**

- Those who are a beginner with no previous knowledge in R programming and SQL.

**Interested to Enroll?**

If yes, then check out the details here-

Applied Data Science with R Specialization

**3. Programming for Data Science with R**– **Udacity**

Rating- | 4.7/5 |

Provider- | Udacity |

Time to Complete- | 3 months( If you spend 10 hours/week) |

This is a **Nanodegree program** offered by Udacity. In this program, there are 3 courses. Course 1 will cover **SQL basics and some advanced SQL queries. **After learning the SQL basics, you have to work on one project where you have to work on investigating a dataset.

The next course will teach **R Programming fundamentals. ggplot2** is a data visualization library in R programming. And you will learn how to use

**ggplot2**to perform data visualization.

The last lesson is **Version Control**. Overall, this Nanodegree has a **perfect balance between theory and practice.** Each lesson has one project. The best part about Udacity is its** technical mentor support feature.** That means, after enrollment, you will get a mentor. And you can ask your doubts with your mentor.

**Extra Benefits-**

- You will get a chance to work on
**real-world**projects from industry experts and Project feedback from experienced reviewers. - You will also get Technical mentor support and Career Services
**.**

**Drawback-**

- The Nanodegree is expensive as compared to other programs.

**Who Should Enroll?**

- Those who are a beginner in data science. There are no prerequisites for this Nanodegree program.

**Interested to Enroll?**

If yes, then check out the details here-

Programming for Data Science with R

**4. **** ****Data Scientist with R**– Datacamp

**– Datacamp**

**Data Scientist with R**Rating- | NA |

Provider- | Datacamp |

Time to Complete- | 88 hours |

This is a **Career Track **offered by **Datacamp. **In this career track, there are 24 courses. At the beginning of this career track, you will learn **R programming basic and advanced concepts** such as **factors, lists, data frames**, etc. Then you will learn **tidyverse**, a powerful and popular collection of data science tools within R.

You will also learn **Data Manipulation and Data joining with dplyr, data visualization using ggplot2, how to import data in R, Data cleaning in R, exploratory analysis in R,** **Statistics and Regression in R, etc.**

Throughout this career track, there are **various self-assessment tests **that will help you to test your understanding. There is one case study in this track where you have to use the ggplot2 package and **explore trends in United Nations voting within each country over time.**

**Drawback-**

- Too much of the code comes pre-written.

**Who Should Enroll?**

- Those who are a
**beginner**and want to**learn data science with R from scratch.**

**Interested to Enroll?**

If yes, then check out the details here-

Data Scientist with R

**5. ****Data Science Specialization**– **Coursera**

**Data Science Specialization**

Rating- | 4.5/5 |

Provider- | Johns Hopkins University |

Time to Complete- | 11 months (7 hours per week) |

This is another specialization program for R programming. In this program, there are **10 courses**. This is a detailed program where you will the** data science basics, R Programming basics, data cleaning, end-to-end data analysis process, etc.**

There are some lessons in this program where you will learn statistical inference, **Regression**, **Statistical Linear Regression Models**, **Multivariable Regression**, **Logistic Regression**, **Poisson Regression, etc.**

This program also covers the **practical part of machine learning, the Caret package,** **Shiny, GoogleVis, Plotly, R Markdown, Leaflet**, **etc.** In the end, there is one Capstone project.

**Courses Details-**

**The Data Scientist’s Toolbox****R Programming****Getting and Cleaning Data****Exploratory Data Analysis****Reproducible Research****Statistical Inference****Regression Models****Practical Machine Learning****Developing Data Products****Data Science Capstone**

**Extra Benefits-**

- You will earn
**Shareable Specialization and Course Certificates**. - Along with that, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

**Drawback-**

- The statistical Inference course is not well-structured and doesn’t explain the concepts easily.

**Who Should Enroll?**

- Those who have beginner-level knowledge in any programming language.

**Interested to Enroll?**

If yes, then check out the details here-

Data Science Specialization

**6. ****Data Science and Machine Learning Bootcamp with R-** Udemy

**Data Science and Machine Learning Bootcamp with R-**Udemy

Rating- | 4.6/5 |

Provider- | Jose Portilla |

Time to Complete- | 17.5 hours |

In this course, first, you will learn **R Programming Basics and advanced R Programming** such as **Math Functions with R**, **Regular Expressions**, and **Dates and Timestamps**.

After that, you will learn **Data Manipulation** and **Data Visualization **using **R Programming. **This course also covers **Linear Regression**, **Logistic Regression**, **K Nearest Neighbors**, **Decision Trees, and Random Forests with R**.

** K-Means Clustering**, **Natural Language Processing**, and **Neural Network with R **are also covered in this course. Overall, this is a good course for learning **data science and machine learning using R programming.**

**Extra Benefits-**

- You will get a
**Certificate of completion**. - Along with that, you will get
**full lifetime access**and**9 articles and 3 downloadable resources.**

**Drawback-**

- Some exercises are hard to understand for beginners.

**Who Should Enroll?**

- Those who are a
**beginner with basic Math Skills.**

**Interested to Enroll?**

If yes, then check out the details here-

Data Science and Machine Learning Bootcamp with R

**7. Data Visualization & Dashboarding with R Specialization- Coursera**

Rating- | 4.9/5 |

Provider- | Johns Hopkins University |

Time to Complete- | 4 months (4 hours per week) |

This specialization program is good for those who want to** learn data visualization using R.** In this program, there are **5 courses.** First, you will learn **R Programming basics and Tidyverse packages**. Next, you will learn** data visualization using ggplot2** and some advanced concepts of data visualization such as **Variations on Scatterplots, Adding Best Fit Lines, Drawing Scatterplot Matrices, Making a stacked area graph, Choropleths, gganimate, etc.**

Next, you will learn **Shiny** and **flexdashboard**. The last part of this program is a **Capstone project.** Overall, this is a good course with lots of exercises and quizzes.

**Courses Details-**

**Getting Started with Data Visualization in R****Data Visualization in R with ggplot2****Advanced-Data Visualization with R****Publishing Visualizations in R with Shiny and flex dashboard****Data Visualization Capstone**

**Extra Benefits-**

- You will earn
**Shareable Specialization and Course Certificates**. - Along with that, you will get

**Drawback-**

- Some exercises are complex. It requires some extra google search.

**Who Should Enroll?**

- Those who are a
**beginner**and want to**learn Data visualization using R programming.**

**Interested to Enroll?**

If yes, then check out the details here-

Data Visualization & Dashboarding with R Specialization

**8. **** Statistical Analysis with R for Public Health Specialization**– Coursera

**Statistical Analysis with R for Public Health Specialization**– Coursera

Rating- | 4.7/5 |

Provider- | Imperial College London |

Time to Complete- | 4 months (If you spend 3 hours/week) |

In this specialization program, there are 4 courses. First, you will understand the **uses of Statistics in Public Health, what is Sampling, and how to Formulate a Research Question.** Then you will learn **R Programming basics and perform Descriptive Analysis** using R. The instructor also explains how to run **a New Hypothesis Test using R.**

In the next two courses, you will learn **Linear Regression and Logistic Regression **using R Programming. The last course is on **Survival Analysis in R**. In this course, you will learn the **KM plot** and **what is Heart Failure, and how to run a KM plot in R.**

**Cox Model and multiple Cox models in R** are also covered in this course. This program is a **combination of practical videos, reading materials, quizzes, and projects.**

**Extra Benefits-**

- You will earn
**Shareable Specialization and Course Certificates**. - Along with that, you will get

**Drawback-**

- The theory of survival analysis part is not covered in detail.

**Who Should Enroll?**

- Those who are a beginner with no knowledge of statistics or R software

**Interested to Enroll?**

If yes, then check out the details here-

Statistical Analysis with R for Public Health Specialization

**Extra Resources to Learn Data Science with R **

**9. **R for Data Science**– Book**

**Author- ***Hadley Wickham & Garrett Grolemund*

This book is for those who want to learn **R programming for data science**. In this book, you will learn how Data Scientists use R. This book will teach **data cleaning, wrangling, visualization, etc.**

But if you are a beginner in R, then I wouldn’t recommend this book. Because this book requires some previous knowledge in R programming.

You can read this book after reading **Hands-On Programming with R**.

**10. Statistics and Statistics with R Tutorials– MarinStatsLectures**

**Number of Videos- **106

In this tutorial, you will learn how to use **R Stats Software for beginners** along with tutorials on the various concepts in statistics.

Along with this, you will learn topics such as **downloading and installing R and RStudio**, **importing data into R, exporting data out of R, getting started working with R**, **descriptive statistics in R, bivariate hypothesis testing using R software both parametric and non-parametric, etc.**

Check the Tutorial here-Statistics and Statistics with R Tutorials

And here the list ends. I hope these Best Online Courses for Data Science with R will help you to learn data science with R Programming. I would suggest you bookmark this article for future referrals.

**My Recommendation**

I would recommend ** Data Science Specialization** by

**Johns Hopkins University**. Because this is a

**detailed course to learn data science from scratch using R Programming.**The best part of this program is that this will cover

**statistical concepts**which are essential in data science.

Now it’s time to wrap up.

**Conclusion**

In this article, I tried to cover all the **Best Online Courses for Data Science with R**. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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Thank YOU!

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## Though of the Day…

–

‘It’s what you learn after you know it all that counts.’John Wooden

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.