Do you want to learn or get better at R programming?
If yes, you will love the list below. I have compiled a list of free and paid R programming courses. Most of them are online and free, so NO excuses about location or money to uplift your skills.
The list includes courses for ALL levels from beginners to advanced users.
The link in the Description column will take you to a general description of the course and the link in the Link to course column will take you to the course website.
|Name of Course||Cost||Company/University||Start Date||Description||Link to course|
|The Analytics Edge||Free||MIT (EdX)||Self-Paced||Link||Link|
|Analyzing Big Data with Microsoft R Server||Free||Microsoft (EdX)||Self-Paced||Link||Link|
|R Programming||Free/$49||Johns Hopkins
|Analyzing Big Data in R using Apache Spark||Free||Cognitive Class||Self-Paced||Link||Link|
|Introduction to R||Free||DataCamp||Self-Paced||Link||Link|
with R Programming
|Free||University College London||Self-Paced||Link||Link|
|Forecasting using R||$29/month||Data Camp||Self-Paced||Link||Link|
|R Basics – R
|Writing Functions in R||$29/month||Data Camp||Self-Paced||Link||Link|
|Advanced R Programming||$49||John Hopkins (Coursera)||05-Feb-2018||Link||Link|
|Statistics Using R||Free||UTAustinX||Self-Paced||Link||Link|
|Building R Packages||$49||John Hopkins (Coursera)||05-Feb-2018||Link||Link|
|R 101||Free||Cognitive Class||Self-Paced||Link||Link|
Are you getting started with R? If yes, make sure to subscribe to the blog. During this year, I’ll be posting approximately every two weeks with tips for people getting started in data analysis with R.
What you’ll learn: An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization.
How to implement all of these methods in R.
An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software.
Learn how to use Microsoft R Server to analyze large datasets using R.
The main component of Microsoft R Server (MRS) is the RevoScaleR package, which is an R library that offers a set of functionalities for processing large datasets without having to load them all at once in the memory. RevoScaleR offers a rich set of distributed statistical and machine learning algorithms, which get added to over time. Finally, RevoScaleR also offers a mechanism by which we can take code that we developed on our laptop and deploy it on a remote server such as SQL Server or Spark (where the infrastructure is very different under the hood), with minimal effort.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
“This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R.
Starting from variables and basic operations, you will learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.
What makes this R programming course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform.
Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.”
Short videos about R programming from Google Developers
Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set. Promoted by John Tukey, exploratory data analysis focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods.
Master Apache Spark, a popular cluster computing framework used for performing large scale data analysis. SparkR provides a distributed data frame API that enables structured data processing with a syntax familiar to R users.
Learn why R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks.
Learn how SparkR, an R package that provides a light-weight frontend, uses Apache Spark from R.
With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40%, and an increasing number of organizations are using it in their day-to-day activities.
In this introduction to R, you will master the basics of this beautiful open source language such as factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis.
“This short course is for anyone who wants to learn the basics of R, and computer programming in general, although the tasks focus on examples from the biosciences. It’s suitable for undergraduates, graduates and researchers from any field that uses statistical computing.”
Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.
“This course was created by R Tutorials. It is meant to give you an introductory understanding of the R language. It takes about 2 hr (+ the time you need to solve the exercises) to complete this course. This is just enough time for a brief introduction.
R programming becomes more and more popular since it is fully open source and reacts very dynamic to new developments.Nowadays it is vital in many scientific or other analytical fields to have a good understanding of the R language. With this course you can build a very solid foundation to later on branch out to the various applications R has to offer.
You will learn about basic commands like “”paste””, “”seq””, “”rep”” and you will also see how graphs are created in R.
We use RStudio as our user interface. You will quickly see that this software makes using R much easier. This course is totally free to you – it is the perfect chance to get familiar with R programming.”
This course taught by Hadley and Charlotte Wickham will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.
The course go into functional programming using the purrr package, handling complicated inputs and creating robust functions.
This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. Upon completing this course you will be able to identify and abstract common data analysis tasks and to encapsulate them in user-facing functions. Because every data science environment encounters unique data challenges, there is always a need to develop custom software specific to your organization’s mission. You will also be able to define new data types in R and to develop a universe of functionality specific to those data types to enable cleaner execution of data science tasks and stronger reusability within a team.
In this first part of a two part course, we’ll walk through the basics of statistical thinking – starting with an interesting question. Then, we’ll learn the correct statistical tool to help answer our question of interest – using R and hands-on Labs.
Finally, we’ll learn how to interpret our findings and develop a meaningful conclusion
Learn the R programming language for data analysis and visualization.
This software programming language is great for statistical computing and graphics.
Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.
R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science. In this course, you’ll be learning about the basics of R, and you’ll end with the confidence to start writing your own R scripts. But this isn’t your typical textbook introduction to R. You’re not just learning about R fundamentals, you’ll be using R to solve problems related to movies data. Using a concrete example makes the learning painless.
You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R’s most important data structures — vectors! From vectors, you’ll then learn about lists, matrix, arrays and data frames. Then you’ll jump into conditional statements, functions, classes and debugging. Once you’ve covered the basics – you’ll learn about reading and writing data in R, whether it’s a table format (CSV, Excel) or a text file (.txt). Finally, you’ll end with some important functions for character strings and dates in R.
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Please help other people to make a good decision, if you’ve taken any of these courses please leave a comment below.