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. Leverage the power of R by completing this free R online course today!
R is open source nature. Anyone can freely adapt the software to whatever platform they choose. Today R runs on almost any standard computing platform and operating system. Indeed, R has been reported to be running on modern tablets, phones, PDAs, and game consoles. Another advantage of R is that is has over many other statistical packages which provides sophisticated graphics capabilities.
R is that platform and thousands of people around the world have come together to make contributions to R, to develop packages, and help each other use R for all kinds of applications. The R-help and R-devel mailing lists have been highly active for over a decade now and there is considerable activity on web sites like Stack Overflow.
Top 6 MOOCS for Learning R Programming Language very quickly as my knowledge
The following is an extensive list of Data Science courses and resources that give you the skills needed to become a data scientist.
The Coursera Data Science Specialization offers a fundamental understanding of data science with the R programming language. It’s recommended that before joining the course you should have some programming experience. It doesn’t mean that you should be familiar to R. And that you have a good understanding of Algebra.
Institution: Johns Hopkins University
Instructors: Brian Caffo, Jeff Leek, Roger D. Peng
Statistical Learning | Stanford Online
This course is very organized and you need 16 weeks time to complete it. If you want to learn statistics with R then this is the right course for you. This is an introductory-level course in supervised learning which gives more focus on regression and classification methods. The syllabus includes linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
Institution: Stanford Online
Instructors: Trevor Hastie and Rob Tibshirani
Learn the R statistical programming language, the lingua franca of data science in this hands-on course. This introduction to R programming course may help you to understand the basics of R. You will learn about basic syntax, variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. You will also 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.
Instructors: Filip Schouwenaars and Jonathan Sanito
This is again an introduction course to R. From this course you will learn the basics of this open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis.
Instructors: Jonathan Cornelissen
Exploratory data analysis is an approach for summarizing and visualizing the important characteristics of a data set.
Instructors: John Tukey
Learn the essentials of R Programming – R Beginner Level! You will learn about the basic structure of R including packages. You will also learn how to handle add on packages, how to use the R help tools and generally how to find your way in the R world. You will also learn how to make basic graphs.
Learn basic R programming from the above course and then try various problems with different datasets by your own.
You can start practicing what you learn by joining Kaggle Competetions.
Check my post What are some good resources for learning R? to get some other important online resources.