R is an open-source programming language that’s optimized for statistical analysis and data visualization. If you want to learn R programming and looking for the best online training courses, then you have come to the right place.
Developed in 1992, R has a rich ecosystem with complex data models and elegant tools for data reporting. At last count, more than 13,000 R packages were available via the Comprehensive R Archive Network (CRAN) for deep analytics.
Best Features of R Programming in Statistics and Data Science
Popular among data science scholars and researchers, R provides a broad variety of libraries and tools for the following:
- Cleansing and prepping data
- Data handling and manipulation.
- Creating visualizations
- Training and evaluating machine learning and deep learning algorithms
- Operators for calculation on arrays.
- A large and coherent collection of tools.
- Graphical facilities for data analysis.
R is commonly used within RStudio, an integrated development environment (IDE) for simplified statistical analysis, visualization, and reporting. R applications can be used directly and interactively on the web via Shiny.
Why Should You Adopt R Programming?
R Programming is the best approach to create reproducible, excessive-quality analysis. It has all of the flexibility and power I’m looking for when dealing with data. Many of the applications I write in R are sincerely just collections of scripts that are equipped into tasks.
It’s utilized in almost every field that you could think of. Nonetheless, the widespread ones comprise – Finance, BioScience, Supply chain, Sports, Retail, Marketing, and Manufacturing.
Many quantitative analysts in finance use R as their major programming instrument. While you get the dangle of it, it is just right for everything from data import and cleaning, exploration, and visualization, doing statistics and analyses. All of the approaches up to trading simulations and production buying and selling functions, relying on what kind of process you are working on.
Google is utilizing R Programming and it is a satisfactory language for doing any form of statistics or data manipulation/visualization, however, IMHO, its power lies in the constructed-in capabilities and libraries instead than its strengths as a language itself (syntax, design, readability, and so forth.)
Best Online Courses on R for Statistics and Data Science
R Programming (Coursera)
4.5 (21,341 ratings) || 635,862 students enrolled
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.
R Programming A-Z™: R For Data Science With Real Exercises! (Udemy)
4.6 (43,519 ratings) || 220,572 students enrolled
R has a steep learning curve and hence, offers step-by-step learning thus making it easier to learn. You get new valuable concepts that can be applied right away through live examples. The training consists of real-life analytical challenges for the learners to solve. The course is for all skill levels and even for people with no programming background.
The program offers the following:
- How to use R Studio
- The core principles of programming
- Learn how to create vectors in R
- Learn how to create variables
- Learning about integer, double, logical, character and other types in R
- Understand 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()
- Know how to install packages in R
- Learn how to customize R studio to suit your preferences
- Understand the Law of Large Numbers
Data Science and Machine Learning Bootcamp with R
(Udemy)
4.7 (14,066 ratings) || 76,270 students enrolled
Data science and machine learning jobs are gaining momentum as it is also high-paying jobs as per Glassdoor. Jose Marcial Portilla, an MS from Santa Clara University is the author of this course and teaches you to use R for data science, machine learning, and data visualization. In addition, you will also learn about advanced features of R, like using R data frames for solving complex tasks and handling Excel files.
R Programming: Advanced Analytics In R For Data Science (Udemy)
4.6 (7,463 ratings) || 53,275 students enrolled
In this course, you will learn:
- How to prepare data for analysis in R
- Performing the median imputation method in R
- How to work with date-times in R
- What Lists are and how to use them
- What the Apply family of functions is
- How to use apply(), lapply() and sapply() instead of loops
- How to nest your own functions within apply-type functions
- Also, how to nest apply(), lapply() and sapply() functions within each other
- And much, much more!
Statistics with R Specialization (Coursera)
4.6 (5,161 ratings) || 111,659 students enrolled
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
R Basics – R Programming Language Introduction (Udemy)
4.7 (16,856 ratings) || 186,145 students enrolled
The course introduces R programming from scratch. It follows a step-by-step approach and thus it is great for beginners. The course begins with setting up development by installing the R and R studio interface, add-on packages, learning to use the R database and R help tools.