R course

10 Best + Free Online R Courses

A growing number of people are learning the R programming language in order to pursue a career as a Data Scientist, one of the most in-demand and well-paid technical positions in the world.
R is a computer language and software environment designed primarily for statistical computations and graphical applications. Since its debut, it has acquired a lot of traction, and it is now the top tool for machine learning, data analysis and visualization, and statistics. A lot of data science job possibilities are being produced every day as a result of the big data boom, and knowing R programming will help you advance your career as a data scientist.
There are numerous resources available when searching for an R programming course or class. However, there are a few free courses that are of good quality. Our specialists have hand-selected the highest-quality R programming certifications, courses, classes, tutorials, and training available online. This list includes both free and paid courses for learners of all levels, from beginners to experts. These are important not only for studying Data Science and Machine Learning but also for anyone learning R programming for graphical and statistical applications.

10 Best + Free Online R Courses

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

There are numerous R courses and lectures available online. R, on the other hand, has a steep learning curve, and trainees frequently become overwhelmed. This course is unique!
This is a true step-by-step course. Every subsequent tutorial builds on what we’ve already learned and takes us one step further. After each video, you’ll discover a new useful topic that you can put into practice right away. The finest thing is that you learn by watching real-life examples.
This course is jam-packed with real-world analytical problems that you will learn to answer. Some of these will be solved together, while others will be assigned as homework.
In conclusion, this course has been developed for students of all skill levels, and you will succeed even if you have no programming or statistical experience!

What you will learn –

  • Learn to program in R at a good level.
  • Learn how to use R Studio.
  • Learn the core principles of programming.
  • Learn how to create vectors in R.
  • Learn how to create variables.
  • Learn about integer, double, logical, character and other types in R.
  • Learn how to create a while() loop and a for() loop in R.
  • Learn how to build and use matrices in R.
  • Learn the matrix() function, learn rbind() and cbind().
  • Learn how to install packages in R.
  • Learn how to customize R studio to suit your preferences.
  • Understand the Law of Large Numbers.
  • Understand the Normal distribution.
  • Practice working with statistical data in R.
  • Practice working with financial data in R.
  • Practice working with sports data in R.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 10.5 hours approx

Instructor – Kirill Eremenko

2. Software Development in R Certification by Johns Hopkins University (Coursera)  

R is a free software environment and programming language for statistical computing and graphics that is widely used by data analysts, data scientists, and statisticians. This Specialization focuses on developing R software for data science applications. As the discipline of data science progresses, it has become evident that software development skills are required to produce and scale relevant data science results and solutions.
This Specialization will provide you with in-depth training in the R programming language, including how to handle complex data, create R packages, and create custom data visualizations. You’ll learn about essential R libraries for data processing, such as tidyverse, as well as data visualization and graphics, such as ggplot2. You’ll learn how to construct tools that are highly reusable, modular, and ideal for usage in a team-based environment or a community of developers using modern software development principles.

What you will learn –

  • Build R packages.
  • Custom data visualization and graphics.
  • Data manipulation and wrangling.
  • Produce and scale data science products.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 24 weeks approx

Instructor – Roger D. Peng, Brooke Anderson

3. Learn Data Science With R (Udemy) 

This is the R programming course, which will introduce Data Science with R. It has over 8.5 hours of material and touches most of the R concepts, which are useful for Data scientists.

What you will learn –

  • Basics of Data Science like.
  • What is Data Science.
  • Data Types.
  • Vectors.
  • Factors.
  • List.
  • Matrices.
  • Data Frames.
  • Read Data from Files.
  • Read Data from oracle Database using RJDBC.
  • Read Data from oracle Database using RODBC.
  • Read Data from oracle Database using ROracle.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 44 hours approx

Instructor – Ram Reddy

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

This course will teach you the fundamentals of R programming. Because learning R to tackle a specific problem helps you remember it better, you’ll utilize a real-world dataset concerning crime in the United States. You’ll gain the R abilities you’ll need to address critical questions about crime disparities between states.
R’s functions and data types will be covered first, followed by how to work with vectors and when to employ advanced functions like sorting. You’ll learn how to use general programming commands like “if-else” and “for loop,” as well as how to manipulate, analyze, and visualize data.

What you will learn –

  • Basic R syntax.
  • Foundational R programming concepts such as data types, vectors arithmetic, and indexing.
  • How to perform operations in R including sorting, data wrangling using dplyr, and making plots.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 8 weeks approx

Instructor – Rafael Irizarry

5. R Programming for Statistics and Data Science (Udemy)

If you want to work as a data analyst or data scientist in your chosen field, you’ll need to learn R programming. What’s more, why wouldn’t you? In the United States, a data scientist is the most popular job title.
However, you’ll need the right equipment and knowledge to do so. R is one of the best programming languages for getting you to where you want to go. When you combine it with statistical knowledge, you’ll be well on your way to landing your dream job.
This course contains all of this and more in one convenient package, and it’s the ideal place to begin your adventure.

What you will learn –

  • Learn the fundamentals of programming in R.
  • Work with R’s conditional statements, functions, and loops.
  • Build your own functions in R.
  • Get your data in and out of R.
  • Learn the core tools for data science with R.
  • Manipulate data with the Tidyverse ecosystem of packages.
  • Systematically explore data in R.
  • The grammar of graphics and the ggplot2 package.
  • Visualise data: plot different types of data & draw insights.
  • Transform data: best practices of when and how.
  • Index, slice, and subset data.
  • Learn the fundamentals of statistics and apply them in practice.
  • Hypothesis testing in R.
  • Understand and carry out regression analysis in R.
  • Work with dummy variables.
  • Learn to make decisions that are supported by the data!

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 6.5 hours approx

Instructor – 360 Careers

6. Statistics with R – Beginner Level(Udemy)

You’ve come to the right place if you want to learn how to use the R program to do basic statistical studies.
Now you don’t have to spend hours searching the internet for information on how to compute statistical indicators in R, create a cross-table, create a scatter plot graphic, or calculate a simple statistical test like the one-sample t test. Everything is explained visually and step by step in this course.

What you will learn –

  • Manipulate data in R (filter and sort data sets, recode and compute variables).
  • Compute statistical indicators (mean, median, mode etc.).
  • Determine skewness and kurtosis.
  • Get statistical indicators by subgroups of the population.
  • Build frequency tables.
  • Build cross-tables.
  • Create histograms and cumulative frequency charts.
  • Build column charts, mean plot charts and scatter plot charts.
  • Build box plot diagrams.
  • Check the normality assumption for a data series.
  • Detect the outliers in a data series.
  • Perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit).

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 3 hours approx

Instructor – Bogdan Anastasiei

7. Programming for Data Science with R Nanodegree Certification (Udacity)

R is quickly becoming one of the most widely used programming languages in the IT sector, particularly in the field of data science. If you’re interested in pursuing a career in Data Science, this course is a good fit for you. This course will teach you how to use SQL, R, and Git, as well as other data science programming tools, to solve real-world data analysis challenges. This curriculum also includes a number of real-world tasks that will allow you to put your knowledge to the test and enhance your skills. This course is part of Udacity’s School of Programming curriculum, which means that once you’ve completed it, you’ll be able to enroll in other nanodegree programs.

What you will learn –

  • Learn SQL fundamentals such as JOINs, Aggregations, and Subqueries. Learn how to use SQL to answer complex business problems.
  • Learn R programming fundamentals such as data structures, variables, loops, and functions. Learn to visualize data in the popular data visualization library ggplot2.
  • Learn how to use version control and share your work with other people in the data science industry.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 12 weeks approx

Instructor – Josh Bernhard,Derek Steer,Juno Lee,Richard Kalehoff

8. R for Data Science Course (LinkedIn Learning)

Programming is broken down into little chunks. You build on fundamental ideas. You transfer your existing knowledge to the next language. R is one of the most popular programming languages for data analysis and reporting, and Lunch Break Sessions teaches it in short lessons that build on what experienced programmers already know.
Each week, spend five minutes creating a building block that you can use in the next two hours at work. Review the basics of the language, learn how to improve current R code, discover new and exciting capabilities, and find valuable development tools and libraries that will make your time programming with R more productive.

What you will learn –

  • In R, you’ll learn the principles of programming.
  • Use R’s conditional statements, functions, and loops to solve problems.
  • In R, you can create your own functions.
  • To get your data into and out of R, follow these steps.
  • Learn how to use R to learn the fundamentals of data science.
  • Use the Tidyverse ecosystem of packages to manipulate data.
  • Explore data in R in a systematic way.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 3 hours approx

Instructor – Mark Niemann-Ross

9. Learn R from Scratch (Educative)

Because this is a text-based interactive course from Educative, it differs from past R programming courses. Because reading is often faster than watching, I prefer text-based courses.
This interactive R programming course assumes no prior knowledge of R and will get you up to speed quickly. You’ll begin with the fundamentals and on to more sophisticated concepts such as exception handling.
The Educative platform has the advantage of allowing you to run code right in your browser, which eliminates the need for any prior setup. This is a significant improvement, as much of the popular software is stalled during installation and setup.

What you will learn –

  • Because this is a text-based interactive course from Educative, it differs from past R programming courses. Because reading is often faster than watching, I prefer text-based courses.
  • This interactive R programming course assumes no prior knowledge of R and will get you up to speed quickly. You’ll begin with the fundamentals and on to more sophisticated concepts such as exception handling.
  • The Educative platform has the advantage of allowing you to run code right in your browser, which eliminates the need for any prior setup. This is a significant improvement, as much of the popular software is stalled during installation and setup.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – Selfplaced

10. R for Data Science: Lunch Break Lessons (LinkedIn Learning)

Programming is broken down into little chunks. You begin by building on fundamental notions. You transfer your existing knowledge to the next language. That’s when these LinkedIn Learning Lunchbreak lessons come in helpful.
R — one of the most popular programming languages for data analysis and reporting — is taught in short sessions that build on existing programmers’ knowledge.
In this series, you’ll go through the foundations of the language, learn how to improve current R code, discover new and fascinating features, and learn about valuable development tools and libraries that will help you get more done with R.

What you will learn –

  • R data manipulation (filter and sort data sets, recode and compute variables).
  • Calculate statistical metrics (mean, median, mode etc.).
  • Determine the kurtosis and skewness of the data.
  • Obtain statistical data for population subgroups.
  • Make tables of frequency.
  • Construct crosstables.
  • Make histograms and frequency graphs with cumulative frequency.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 12 hours approx

Instructor – Mark Niemann-Ross

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