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 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|
|Statistical Learning||Free||Stanford Online||12-Jan-2016||Link||Link|
|R Programming||Free/$49||Johns Hopkins
|$2,190||NYC Data Science Academy||22-Oct-2016||Link||Link|
|Introduction to R||Free||DataCamp||Self-Paced||Link||Link|
with R Programming
|Free||University College London||Self-Paced||Link||Link|
|Learning To Program
|$49.50||Infinite Skills, Inc||Self-Paced||Link||Link|
|R Basics – R
|Statistics Using R||Free||UTAustinX||2-Feb-2016||Link||Link|
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.
“This is an introductory-level course in supervised learning, with a 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).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). The pdf for this book is available for free on the book website.”
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.
This intensive Data Science with R – Beginner Level course being offered by NYC Data Science Academy is a five week course that will introduce you to the wonderful world of R and provide you with an excellent understanding of the language that leaves you with a firm foundation to build upon.
Why R is important: R is a powerful, comprehensive, and dynamic programming language that, since its release in 1996, is on course to eclipse traditional statistical packages as the dominant interface in computational statistics, visualization, and data science.
And another thing: it’s free! As an open-source platform, R has grown to become an incredibly flexible tool that can be applied to nearly every graphical and statistical problem. The community of R users is continuing to build new functionality to the language, and R is often the first statistical tool to provide support for new algorithms and cutting-edge methods in data science.
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.”
In this Learning R training course, expert author Stuart Greenlee will teach you how to use R, a programming language used for statistical computing and graphics. This course is designed for beginners that have no previous R programming experience. You will require a fundamental understanding of statistics to get the most out of this course.
You will start by learning how to install and navigate R studio, then move into learning basic operations like statistical functions, matrix operations, and string functions. Stuart will show you how to plot, including scatter plots, probability plots, and plotting arguments.
This video tutorial will cover working with data and data analysis, such as extracting model information, examining files and objects, and subsetting and indexing. You will also learn about conditional statements and user-defined functions, including how to write and de-bug functions.
Finally, you will learn how to save different types of data.Once you have completed this computer based training course, you will be fully capable of using R for developing statistical software and data analysis tools. Working files are included, allowing you to follow along with the author throughout the lessons.
“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.”
R is an extraordinarily powerful language with a vast community of great resources, but where should you start when all you want to do is get your data into a usable format? How do you know your data might be ready? What are the pitfalls you should watch for so that you don’t perform an analysis on bad data?
This course will teach you from start to finish how to get your data into R efficiently and polish it up so that it is as good as it can be. This will let you or your team focus after this step on the statistical modeling, visualization, reporting, sharing, or any other post-processing task you wish to perform. Confidence, reliability, and reproducibility in your data acquisition and preparation are the kingpins to being able to maximize your data’s value.
In this online course, “R Programming Intro 1”, you will be introduced to basic concepts in computer programming via R – it is for those who have had little or no experience in programming.
You will learn how to get going in R from the beginning, understand file formats and basic R syntax, and learn about using text editors to write code.
You will learn how to read in files, use symbols and assignments, and iterate simple loops.
The course closes with discussion of data structures and subsetting.
Note: Those with some familiarity with programming should probably start with R Programming Introduction 2.
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.
“What you’ll learn:
How to use R to perform basic statistical analyses
Why R has become the tool of choice in bioinformatics, health sciences and many other fields
How to use peer reviewed packages for solving problems at the frontline of health science research
How to make a suitable choice between a few common statistical methods, based on the type of problem and a given data set”
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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.