11 of The Best Online Courses on R Programming (2024)

In this article you will find the best courses on programming in R, available on the main e-Learning platforms in the world. Courses of different levels, which will give you a certificate.
The Best Virtual Courses on Programming in R

If you have come this far after a Google search for "The Best Online Courses on R Programming" or a similar search, we give you an answer to start by defining the R programming language as a free software environment (account license GNU GLP) and interpreted code language, what eu means that the R programming language executes the instructions directly, without prior compilation of the program to machine language instructions.

The R programming language is one of the most used languages ​​in the data science or Big Data industry, thanks to its special data visualization features and a wide variety of statistical tools.


What is the programming language in R

According to Wikipedia, R is a programming language and environment with a focus on statistical analysis. R was born as a free software reimplementation of the S language, added with support for static scope. It is one of the most widely used programming languages ​​in scientific research, and is also very popular in the fields of machine learning, data mining, biomedical research, bioinformatics, and financial mathematics. The possibility of loading different libraries or packages with calculation and graphing functionalities contributes to this.

With the R programming language you can make linear and logistic regression models, time series analysis, classic statistical tests, grouping, clustering, classifications and countless other statistical techniques.

Take advantage: Annual Coursera Plus with $100 discount. USD $299 for a limited time! Click and find out how.

Without a doubt, R is one of the most used languages ​​in statistical research. as it allows researchers to produce high-quality graphics quite easily, including mathematical symbols and formulas, whenever necessary.

Characteristics of the programming language in R to study?

The R programming language has a powerful set of statistical and graphical tools that allow programmers to define their own functions. This is why the R programming language is very popular in projects focused on:

  • Scientific investigation.
  • Data manipulation.
  • Statistic analysis.
  • Artificial intelligence.
  • Automatic learning or Machine Learning.
  • graphic techniques.
  • Modeling and predictions.
  • Financial mathematics.
  • Bioinformatics.
  • Biomedical research.

Courses and programs on programming in the R language that address these topics will be ideal for making the most of its possibilities.

Find in this article, information about Programming Courses in R.
Virtual courses, MOOCs, and other types of advanced virtual studies such as Professional Certificates, Specialized Programs, Expertrack, Microcredentials, among other study formats developed by the main e-Learning platforms in the world.

From world-renowned platforms such as Coursera, edX, Future Learn, Udemy, Linkedin Learning, CFI, Edureka, among others, find in this post courses highly valued by thousands of previous students, which will help you make better decisions about which one to choose. the most convenient course for your goals.

Where to study a programming course in R?

R programming courses can be found from many sources, including YouTube. Perhaps some of the web-based platforms provide high-quality studies. However, at AulaPro we have chosen the platforms that we believe are the most important worldwide in online studies, managing to go beyond what can be obtained with a video, by developing a state-of-the-art learning experience, made up of videos. , downloadable documents, online exams, virtual projects, simulators and sandbox, and finally, an effort reward with a Certificate of Completion, which will generally cost a fraction of what it could really be valued based on the quality of its content.

These low prices can only be given thanks to the learning environment offered by online education, and which in turn has allowed those who develop these studies to reach tens of thousands of students in various parts of the world. In other words, the scalability offered by eLearning allows the cost of creating a virtual course with high academic content, like all the ones you will find in this list, to be distributed among many more students than the group of 20 or 30 students who could attend a face-to-face class.

In this sense, the studies on this list are developed by prestigious universities that occupy the first places in international rankings, by world-renowned technology companies, by international experts of proven effectiveness as trainers, and are made available to the public through the use of platforms with the most advanced technological developments, in terms of learning experience, or by platforms specialized in the subject.

The courses have different levels of depth. From short courses that address a specific topic with a dedication time, which will not exceed 6 weeks, to robust study programs that a period of 6 to 10 months, can leave the student with a deep knowledge and with even the ability to give a turning point in his professional career.

Take advantage: Annual Coursera Plus with $100 discount. USD $299 for a limited time! Click and find out how.

In this article you will find programming studies in R of:

  • Coursera
  • LinkedIn Learning
  • Future learn
  • Udemy
  • edX
  • edureka



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Category: Data Science -- Developed by: Johns Hopkins University

Master data visualization with R

Dive into the world of data visualization with “Data Visualization with R Specialization,” a program designed for students eager to convey data through compelling visual stories. This five-course journey harnesses the statistical power of R to create engaging static images and dynamic interactive dashboards suitable for a variety of audiences.

Basic components of the specialization

From data to visual stories in R

Use R to transform raw data into compelling narratives. This specialization covers everything from fundamental visualization techniques to sophisticated interactive dashboards, ensuring you can present data in the most impactful way.

Practical learning project 

Explore various visualizations: Experiment with various visualization methods for presenting data, improving your ability to create images tailored to specific data sets and audience needs.

Create shareable research products: Focus on developing reproducible and shareable visualizations, promoting collaboration and expanding the scope of your knowledge.

Key Results Upon completion of this specialization, you will be adept at designing meaningful visualizations, tailoring your presentations to diverse audiences, and publishing your insights online, making your data stories accessible and engaging.

Finally, this “data visualization with R specialization” is your path to mastering data storytelling. Whether you want to improve your data science skills or are simply passionate about visualizing data, this program offers the tools and knowledge to turn complex data sets into compelling narratives. Start your data visualization journey today and bring your data stories to life.

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Category: Computer Science -- Developed by: Johns Hopkins University

Ask the right questions, manipulate data sets, and create visualizations to communicate results.

This specialization covers fundamental data science tools and techniques, including fetching, cleaning, and exploring data, programming in R, and conducting reproducible research. Students who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.

The five courses in this specialization are the same courses that make up the first half of the Data Science Specialization. This specialization is offered for students who want to start and complete the fundamental part of the curriculum first, before moving on to the more advanced topics in data science: statistics and machine learning.

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Category: Computer Science -- Developed by: Google

The seventh course of the Google Data Analytics Certificate is this. You will gain the knowledge and skills necessary to apply for an entry-level data analyst job after taking these courses. You will study R, a programming language, in this course. You will learn to operate with R in the environment provided by RStudio. Software tools and programs that are unique to R, including R packages, will also be covered in this course. You will learn how R enables you to more effectively clean, organize, analyze, visualize, and report data. Google's current data analysts will continue to guide you and provide practical examples of how to perform typical data analyst jobs using the best tools and resources.

Students who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is required.

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This course is part of Google Data Analytics Professional Certificate.

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Category: Computer Science -- Developed by: Udemy

Interested in the field of machine learning? Then this course is for you! This course has been designed by two professional data scientists so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in an easy way.

We will guide you step by step in the world of machine learning.

With each tutorial, you'll develop new skills and improve your understanding of this challenging but lucrative subfield of data science.

This course is fun and exciting, but at the same time, we delve into machine learning.

It is structured as follows: Part 1 – Data Preprocessing Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Part 3 – Classification: Logistic Regression, K -NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 – Clustering: K-Means, Hierarchical Clustering Part 5 – Association Rule Learning: Apriori, Eclat Part 6 – Learning by Reinforcement: Upper Confidence Limit, Thompson Sampling Part 7 – Natural Language Processing: Bag-of-Words Model Algorithms for NLP Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 – Dimensionality Reduction: PCA, LDA, Core PC Part 10 – Model Selection and Strengthening: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Also, the cu rso is packed with practical exercises based on real-life examples.

This course reached 5th place in our list of the best Udemy courses of all time.

Category: Computer Science -- Developed by: IBM

This Professional Certificate program is designed for anyone looking to develop skills, tools, and a portfolio of work for a position as an entry-level data analyst or data scientist. No prior knowledge of R or programming is required to get started!

In this professional data analysis and visualization with Excel and R certification program, you'll immerse yourself in the role of a data analyst or data scientist and develop the skills needed to work with a variety of data sources and apply powerful tools, such as Excel, Cognos Analytics, and the R programming language (including: ggplot2, Leaflet, and R Shiny), to become a data-driven professional and gain a competitive advantage in the job market.

By the end of this program, you will be able to explain the roles of data analyst and data scientist.

Category: Computer Science -- Developed by: Udemy

By doing, you can learn to program in R!

There are numerous R courses and conferences available. R, on the other hand, has a very steep learning curve and students often feel overwhelmed. This course is unique!

This is a true step-by-step course. Each new tutorial builds on what we've already learned and takes us one step further.

After each video, you will have learned a valuable new concept that you can immediately apply. The best part is that you learn from real life examples.

This course is packed with real-world analytical challenges that you will learn to solve. Some of these will be solved together, while others will be assigned to you as homework.

In short, this course has been designed for all skill levels and even if you have no background in programming or statistics, you will be successful in this course!

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Category: Computer Science -- Developed By: Duke University

You will learn how to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-driven decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, and critique data-based claims in this specialization.

Category: Computer Science -- Developed by: Edureka!

Data Analysis with R training will help you develop expertise in R programming, data manipulation, exploratory data analysis, data visualization, data mining, regression, sentiment analysis, and using R Studio for business case studies. real life in retail and social networks.

edureka's Data Analytics with R training course is specifically designed to provide the knowledge and skills needed to become a successful analytics professional. It starts with fundamental concepts like data manipulation and exploratory data analysis before moving on to more advanced topics like ensemble decision trees and collaborative filtering.

The Data Analytics with R training certifies you as an expert in the most widely used analysis tool. “R” is the tool of choice for data scientists due to its statistical power, graphing ability, low cost, and rich suite of packages.

Category: Computer Science -- Developed by: Harvard University

The R programming language is taught in the context of statistical data and statistical analysis in the life sciences in this course.

We will learn the fundamentals of statistical inference and how to compute values ​​or confidence intervals while analyzing data with R code. We provide examples of R programming in such a way that the connection between concepts and implementation is clear. R programming problem sets will be used to test understanding of and ability to implement basic data analysis.

We will use visualization techniques to investigate new data sets and determine the best approach. When the data does not fit the assumptions required by standard approaches, we will describe robust statistical techniques as alternatives. You will learn the fundamentals of conducting reproducible research by analyzing data with R scripts.


Category: Computer Science -- Developed by: Edureka!

Edureka's R Certified Data Science Training teaches you how to use R to master machine learning algorithms such as K-Means clustering, Decision Trees, Random Forest, and Naive Bayes.

This Data Science with R Training course covers statistics, time series, text mining, and an introduction to deep learning. Throughout the R programming course, you will implement real-world use cases from the media, healthcare, social media, aviation, and human resources industries.

Data science is defined as a “concept for unifying statistics, data analysis, and their related methods” to “understand and analyze real phenomena” using data. Data Science Training employs techniques and theories drawn from a variety of fields within the broad areas of mathematics, statistics, data science, and computer science, including machine learning, classification, cluster analysis, data mining, data and visualization.

The data science certification course teaches you how to analyze and visualize various data sets, as well as different machine learning algorithms such as K-Means clustering, decision trees, random forest, and Naive Bayes.

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Category: Computer Science -- Developed by: Johns Hopkins University

This course will teach you how to program in R and how to use R for effective data analysis. You will learn how to install and configure the software required for a statistical programming environment, as well as how to describe generic programming language concepts as they are implemented in a high-level statistical language.

The course covers practical problems in statistical computing, such as programming in R, reading data in R, accessing R packages, writing R functions, debugging, profiling R code, organizing and commenting R code, etc. Examples of topical work in statistical data analysis will be provided.

Frequently Asked Questions (FAQs) about Programming in R

No prior programming experience is necessary to begin most R programming courses.

Many of these courses are designed to introduce students to the R programming language from a basic level, offering programming and statistics fundamentals for those without prior experience.

Completing an R programming course typically earns you a Certificate of Completion. These certificates are issued by the e-learning platform or the academic institution that offers the course and may vary in form and validity.

Many of these certificates are recognized by the industry and can be useful in improving your CV and job prospects.

Yes, it is possible to learn R on your own using free resources available online such as tutorials, videos, and official R documentation.

However, a structured course could offer you a more coherent and systematic learning path, as well as access to practical projects, exams, and possibly interaction with instructors and other students.

The time required to learn R to a proficient level can vary depending on the time you spend studying and your previous experience with programming and statistics. Courses can vary from a few weeks to several months.

To gain in-depth, practical knowledge, you may need to dedicate several months of study and practice.

R is a language specifically designed for statistical analysis and data visualization, with a vast collection of packages and libraries available. It is especially strong in statistics and is widely used in academic research, data science, and biotechnology, among other fields.

Although other languages, such as Python, are also popular in the field of data analysis, R is preferred by those looking for a more specialized solution for statistics.

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