Statistics with R Certification by Duke University (Coursera) ranks first on the list of best online R Programming courses. Statistics with R certification is one of the most effective courses for learning statistics with R. You'll learn how to analyze and visualize data in R, as well as how to create reproducible data analysis reports. Introduction to Probability and Data, Inferential Statistics, Linear Regression and Modeling, Bayesian Statistics, and Statistics with R Capstone are the five courses in this R statistics specialization.

The capstone project will be a R analysis that answers a scientific/business question provided by the course team. Learners will be given a dataset for analysis, and they will be required to apply various methods and techniques learned in previous courses. Dr. Mine Etinkaya-Rundel, along with three other Professors from Duke University's Department of Statistical Science, is the program's main instructor. This program requires no programming experience, only basic math skills and a genuine interest in data analysis.

Key Highlights

• Gain statistical mastery of data analysis including inference, modelling and Bayesian approaches
• Learn to wrangle and visualize data with R packages for data analysis
• Understand simple and multiple linear regression models
• Perform frequentist and Bayesian statistical inference and modeling to make data-based decisions
• Gain expertise needed to apply for statistical analysis or data scientist positions
• Plenty of practice exercises and tests
• Access to forum with great help to solve doubts

Duration : Approx. 7 months, 4 hours per week

Rating : 4.7

Enroll here: coursera.org/specializations/statistics

R Programming: Advanced Analytics In R For Data Science (Udemy) ranks 2nd on the list of best online R Programming courses. If you have a basic understanding of the R programming language and want to advance your skills, this is the R programming course for you. It focuses on data science and analytics, as well as statistical analysis in R. Kirill Eremenko, the instructor, walks you through the complex concepts in a very simplified and easy-to-understand manner.

The course is divided into 51 lectures that cover data preparation, lists in R, and the "Apply" family of functions in depth. You will learn how to prepare data for analysis in R, use the median imputation method, work with date-times in R, use lists in R, use apply functions instead of loops, nest user defined functions with apply-type functions, and so on. This course is not for complete beginners, and assumes basic knowledge of R. Knowledge of GGPlot2 package, dataframes, vectors and vectorized operations is also recommended.

Key Highlights

• Advanced level course for those who want to dive deep in R
• Professional R Video training
• Unique datasets designed with years of industry experience in mind
• Learn to create a timeseries plot in R
• Understand how the Apply family of functions works
• Locate missing data in your dataframes
• Learn to apply Factual Analysis method, Median Imputation method to replace missing records
• Engaging exercises to help correlate analytics in the real world

Duration : 6 hours on-demand video

Rating : 4.7

Enroll here: udemy.com/course/r-analytics

Learning R (LinkedIn Learning – Lynda) ranks 3rd in the list of best online R Programming courses. If you want to participate in the data revolution, you need the right tools and skills. R is a free, open-source language for data science that is among the most popular platforms for professional analysts. Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power.

You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. This is an introductory course that will teach you the R programming language. It begins with instructions for installing R, configuring the R environment, and using R Studio. The course then covers how to read data from spreadsheets and SPSS, as well as how to use and manage packages for advanced R functions.

This course is taught by Barton Poulson, a professor, designer, and data analytics expert. He effectively walks you through several examples of how to create charts and plots, test statistical assumptions and data reliability, look for data outliers, and use other data analysis tools. By the end of the course, you’ll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.

Key Highlights

• Learn to use charts, such as histograms, bar charts, scatter plots, and box plots, to get the big picture of your data
• Learn descriptive statistics such as means, standard deviations, and correlations for a more precise depiction
• Learn inferential statistics like regression, t-tests, the analysis of variance, and the chi-square test to determine the reliability of your results
• Learn to create beautiful presentation charts to share your analysis results
• Several engaging exercises available with data sets which can be downloaded
• View Offline mode allows learners to download courses on mobile devices and watch them on the go without an internet connection

Duration : 2 hours 25 minutes

Rating : 4.6

Enroll here: linkedin.com/learning/learning-r-2

R Programming Course A-Z™: R For Data Science With Real Exercises (Udemy) ranks 4th on the list of best online R Programming courses. This is one of the best R programming tutorials, with live examples, that will help you master programming in R and R studio. Data analytics, data science, statistical analysis, packages, functions, and GGPlot2 will all be covered. This Udemy R course has been taken by over 96,000 students. The course makes no assumptions about prior knowledge or experience. It is designed in such a way that even those with no statistical background can succeed. It guides you through R's steep learning curve step by step. You will practice the skills you learn in the course using specially designed datasets.

The course begins by teaching the fundamentals of R programming and how to combine programming and statistical concepts. The course then moves on to more advanced topics such as matrices and data frames. To support learning, all course material is intertwined with plenty of theory and real-life examples. Every tutorial will teach you a new valuable skill, and each section will explain how to apply that skill to solve real-world problems.

Key Highlights

• Create visualizations to best capture your analysis and captivate your audience
• Learn to solve real life analytical challenges
• Learn how to customize R studio to suit your preferences
• Learn how to create and use vectors and matrices in R
• Learn how to install packages in R
• Practice working with financial, statistical and sports data in R
• Know all about Normal distribution and Law of Large Numbers
• Homework exercises for extra practice

Duration : 10.5 hours on-demand video

Rating : 4.6

Enroll here: udemy.com/course/r-programming/

Data Science and Machine Learning Bootcamp with R (Udemy) ranks 5th in the list of best online R Programming courses. This R language course will teach you how to program in R, how to analyze data in R, how to create stunning data visualizations, and how to use Machine Learning with R. Jose Portilla, one of the best instructors on Udemy who has taught thousands of students about Data Science and Programming, created and teaches the course. The program is designed for both experienced professionals who want to change careers and complete beginners who want to learn data science and machine learning from the ground up.

This is a comprehensive R course that includes over 100 HD video lectures, detailed code notebooks for each lecture, 8 articles, and three downloadable resources. It starts with environment setup and then moves on to the fundamentals of programming in R, including vectors, matrices, and data frames. The course then moves on to data visualizations in R, culminating in a data Capstone project. The course also delves into machine learning with a dozen portfolio projects. When you finish the course, you will be given a certificate of completion.

Key Highlights

• Create Data Visualizations
• Use R to manipulate data easily
• Use R to handle csv, excel, SQL files or web scraping
• Learn machine learning algorithms including topics like Linear regression, Logistic regression and more advanced topics such as decision tress, random forests and support vector machines
• Variety of R programming exercises, capstone projects and Machine Learning portfolio projects
• Access to online Q&A forum
• Explore data mining of Twitter for tending topics and creating a word cloud of these topics

Duration : 17.5 hours on-demand video

Rating : 4.6

Enroll here: udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/

R Programming Certification from Johns Hopkins University (Coursera) ranks 6th on the list of best online R Programming courses. This course is part of Johns Hopkins University's Data Science Specialization. Its goal is to teach R as a programming language as well as how to use R for effective data analysis. It covers topics such as reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

This R programming certification begins with the fundamental building blocks of R, such as datatypes, functions to read and write data, and so on. It then goes on to explain how to write R programs using control structures, R functions, and basic data operations. In addition, you will learn about code profiling and debugging. The course also looks at how to simulate data in R, which is used as the foundation for simulation studies. Roger D. Peng, PhD, Associate Professor of Biostatistics, Jeff Leek, PhD, Associate Professor of Biostatistics, and Brian Caffo, PhD, Professor of Biostatistics teach the course.

Key Highlights

• Learn how to install and configure software necessary for a statistical programming environment
• Cover the history of R and S
• Learn to collect detailed information using R profiler
• Understand programming language concepts and their implementation in R
• Make use of R loop functions and debugging tools
• Comes from highly reputed university and highly acclaimed professors

Duration : Approx. 20 hours

Rating : 4.6

Enroll here: coursera.org/learn/r-programming

This is the first course in HarvardX's 9-part Data Science professional certificate program on the edX platform. The purpose of this edX R course is to introduce students to the fundamentals of R programming. This course has no prerequisites, so it is appropriate for anyone just starting out in the field of data science. It is also useful for anyone who has programming experience in another language but wants to learn R.

This R programming certification course teaches how to solve real-world problems with R using a real-world dataset about crime in the United States. It goes over R's functions and data types, as well as vector operations and advanced functions like sorting. You'll learn how to use general programming features such as conditional constructs "if-else" and "for loop" commands, as well as how to manipulate, analyze, and visualize data.

Rafael Irizarry, the course instructor, does an excellent job of explaining topics in simple terms, making even complex topics understandable. There are several programming assignments to help you solidify your knowledge. The course is free, but you must pay a small fee for graded exams and a certificate of completion.

Key Highlights

• Build a strong foundation to prepare for more in-depth courses
• Learn data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub and reproducible document preparation with RStudio
• Learn to perform operations in R including sorting and making plots
• Learn to solve problems using real life dataset

Duration : 8 weeks, 1 to 2 hours per week

Rating : 4.6

Enroll here: edx.org/course/data-science-r-basics

This R Certification program provides the most in-depth introduction to R programming for Statisticians and Data Scientists. The goal of this course is to take you from a complete beginner in R to an expert professional who can perform data manipulation on demand. You will be introduced to programming fundamentals, data manipulation techniques and tools, and data visualizations and plots. It also includes a step-by-step statistics tutorial.

This R programming class is an excellent combination of theory and practice. It is necessary to take care to gradually increase your theoretical knowledge and practical skills. Several exercises in the course help to reinforce your learning. It also includes homework and projects to challenge the students further. You will receive a verifiable certificate upon completion of the course.

Key Highlights

• Learn descriptive statistics and fundamentals of inferential statistics
• Master confidence intervals and hypothesis testing, as well as regression and cluster analysis
• Learn to work with vectors, matrices, data frames, and lists
• Become adept in ‘the Tidyverse package’ enabling you to index and subset data
• Learn the grammar of graphics and the ggplot2 package
• Learn how to visualise data – plot different types of data & draw insights
• Learn complete skill set to tackle a new Data Science project
• Learn to make decisions that are supported by the data

Duration : 6.5 hours on-demand video

Rating : 4.5

Enroll here: udemy.com/course/r-programming-for-statistics-and-data-science

This Nanodegree program prepares students for a career in data science by introducing them to the fundamental data programming tools of R, SQL, command line, and git. The program is designed for beginners and is divided into three courses: Introduction to SQL, Introduction to R Programming, and Introduction to Version Control. Throughout the program, students complete three projects, with a focus on the R programming language.

In the R programming module, you will begin by understanding common R use cases and why it is popular, as well as installing and configuring the R environment. You will learn to use R data types and variables to represent and store data, as well as conditionals and loops to control the flow of programs. You'll also learn about complex data structures, such as lists, which are used to store collections of related data. You'll also learn how to write your own custom functions, scripts, and error handling. Data visualization with R libraries is also thoroughly covered.

Key Highlights

• Learn the most important programming languages (R and SQL) used by the data scientists
• Learn to make beautiful visualizations using the ggplot2 library
• Use the popular diamonds dataset to put your R skills to work
• Industry relevant projects to gain hands-on experience
• Personalized feedback on projects from network of 900+ project reviewers
• Get access to student hub to connect with fellow learners
• Get access to technical mentor support and career support services
• No prior experience requirement to enrol for the program

Duration : 3 months, 10 hours per week

Rating : 4.5

Enroll here: udacity.com/course/programming-for-data-science-nanodegree-with-R--nd118

This R programming specialization provides rigorous R language training as well as best software development practices for building data science tools that are not only robust, modular, and reusable but also collaborative (thus suitable for use in team based and community environments). This program will teach you how to handle complex data, create R packages, and create custom data visualizations.

There are five courses in this R language certification program. It begins with an introduction to R (essential R foundational concepts) and progresses to advanced topics such as functional programming, object-oriented programming, error handling, user functions, R packages, and software maintenance. It concludes with a R programming Capstone project. Throughout the program, the emphasis will be on aspects of the R language that are useful for creating tools and code that can be used by others. The course assumes prior programming experience (in any language) and working knowledge of mathematics through algebra.

Key Highlights

• Gain fluency at the R console
• Be able to create tidy datasets from a wide range of possible data sources
• Learn to define new data types in R and develop a universe of functionality specific to those data types
• Learn how to distribute packages via CRAN and GitHub
• Create new visualization building blocks using the ggplot2 framework

Duration : Flexible

Rating : 4.4

Enroll here: coursera.org/specializations/r