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Best R Programming Courses on Udemy

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Best R Programming Courses on Udemy
Best R Programming Courses on Udemy

In this article, you will find the Best R Programming Courses on Udemy & advanced like Beginner courses, and Practice test coursesSo, check these Best R Programming Courses on Udemy and find the Best R Programming Courses on Udemy for Beginners to Advanced according to your need. In the previous article, I have shared the best free Data Analysis courses for Beginners to advance that help you get practical skills with those courses.

Here we have covered the Best R Programming Courses on Udemy. Let’s go through the list of Udemy R Programming Courses one by one.

Best R Programming Courses on Udemy 2022

R Programming A-Z: R For Data Science With Real Exercises

Rating: 4.7

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

  • 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

Best for: Beginners | Course Duration:10.5 hrs | Provider: Udemy

Total Articles:7

Info: Visit this course and get amazing offers on this Course

Data Science and Machine Learning Bootcamp with R

Rating: 4.8

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

You can learn how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning.

  • Programming with R
  • Advanced R Features
  • Using R Data Frames to solve complex tasks
  • Use R to handle Excel Files
  • Web scraping with R
  • Connect R to SQL
  • Use ggplot2 for data visualizations
  • Use plotly for interactive visualizations
  • Machine Learning with R, including:
  • Linear Regression
  • K Nearest Neighbors
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Data Mining Twitter
  • Neural Nets and Deep Learning
  • Support Vectore Machines

Best for: Beginners | Course Duration:17.5 hrs | Provider: Udemy

Total Articles:9 | Total Downloadable Resources: 7

Info: Visit this Course and get amazing offers on this Course

R Programming – R Language for Absolute Beginners

Rating: 4.6

This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups: 

  1. Introductory slides lectures with the most well-known commands for each type of R object.
  2. Code along lectures where you will see how we can implement the stuff we will learn!
  3. Test your knowledge with questions and practical exercises with different levels of difficulty!
  4. Analyze real datasets and understand the thought process from question to R code solution!

This course was designed to be focused on the practical side of coding in R – instead of teaching you every function and method out there, I’ll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects. 

At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis.

Best for: Beginners | Course Duration:17.5 hrs | Provider: Udemy

Total Articles:9 | Total Downloadable Resources: 7

Info: Visit this Course and get amazing offers on this Course

Data science with R: tidyverse

Rating: 4.6

is one of the most in-demand programming languages when it comes to applied statistics, data sciencedata exploration, etc. If you combine R with R’s collection of libraries called tidyverse, you get one of the deadliest tools, which was designed for data science-related tasks. All tidyverse libraries share a unique philosophygrammar, and data types. Therefore libraries can be used side by side, and enable you to write efficient and more optimized R code, which will help you finish projects faster.

This course includes several chapters, each chapter introduces different aspects of data-related tasks, with the proper tidyverse tool to help you deal with a given task. Also, the course brings to the table theory related to the topic, and practical examples, which are covered in R. If you dive into the course, you will be engaged with many different data science challenges, here are just a few of them from the course:

  • Tidy data, how to clean your data with tidyverse?
  • Grammar of data wrangling.
  • How to wrangle data with dplyr and tidyr.
  • Create table-like objects called tibble.
  • Import and parse data with readr and other libraries.
  • Deal with strings in R using stringr.
  • Apply Regular Expressions concepts when dealing with strings.
  • Deal with categorical variables using forcats.
  • Grammar of Data Visualization.
  • Explore data and draw statistical plots using ggplot2.
  • Use concepts of functional programming, and map functions using purrr.
  • Efficiently deal with lists with the help of purrr.
  • Practical applications of relational data.
  • Use dplyr for relational data.
  • Tidy evaluation inside tidyverse.
  • Apply tidyverse tools for the final practical data science project.

Best for: Beginners | Course Duration: 30 hrs | Provider: Udemy

Total Articles: 2 | Total Downloadable Resources: 197

Info: Visit this Course and get amazing offers on this Course

R Programming For Absolute Beginners

Rating: 4.8

The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.   

In the first section of this course you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables.  

The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more.

These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling.  

After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions.

Best for: Beginners | Course Duration: 30 hrs | Provider: Udemy

Total Articles: 2 | Total Downloadable Resources: 197

Info: Vist this Course and Get amazing offers on this Course

Statistics for Data Analysis Using R

The course will teach you the basic concepts related to Statistics and Data Analysis,  and help you in applying these concepts. Various examples and data sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use R to perform these calculations.

The following areas of statistics are covered:

Descriptive Statistics – Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)

Data Visualization – 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)

Probability – Basic Concepts, Permutations, Combinations (Basic theory only)

Population and Sampling – Basic concepts (theory only)

Probability Distributions – Normal, Binomial  and Poisson Distributions (Base R functions and the visualize package)

Hypothesis Testing – One Sample and Two Samples – z Test, t-Test, F Test, Chi-Square Test

ANOVA – Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R.

Best for: Beginners | Course Duration: 30 hrs | Provider: Udemy

Total Articles: 2 | Total Downloadable Resources: 197

Info: Visit this Course and get amazing offers on this Course

Complete Machine Learning with R Studio – ML for 2022

This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an in-depth understanding of machine learning and predictive modelling techniques using R.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Best for: Beginners | Course Duration: 30 hrs | Provider: Udemy

Total Articles: 2 | Total Downloadable Resources: 197

Info: Visit this Course and get amazing offers on this Course

And here the list ends. So, these are the Best R Programming Courses on Udemy for Beginners to Advanced. I will keep adding more Best R Programming on Udemy to this list.


I hope these Best R Programming Courses on Udemy for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.

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