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Introduction

R is a programming language, statistical computation, and programming environment used for data analysis, graphical representations, and reporting. It was first developed by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. Currently, the R programming language is developed by the R Development Core Team. R provides a wide range of features such as linear and non-linear modeling, time-series analysis, classification, clustering, data visualization, and graphical techniques.

R is available as an open-source programming language under GNU General Public License with the support for cross-platform interoperability, meaning it has distributions that run on Windows, Linux, and Mac.

Nevertheless, enhancing the skill set with the R programming language offers many opportunities as a data scientist, data analyst, data architect, geo statistician, and more excellent career prospects across various industries such as healthcare, academics, finance, and more.

Data Science- Foundations using R Specialization by John Hopkins University – Coursera

This is one of the top online courses available on R programming on Coursera. It is a beginner-oriented specialization course covering the foundations of data science tools and techniques for acquiring data, cleaning, and exploring data using the R programming language. The learners will complete industry-related projects at the end of each course in the specialization.

Some of these projects include R for cleaning data, performing analysis. In addition, there are peer-reviewed assignments to give a complete learning experience and a thorough understanding of the concepts and implementations. Finally, the learners will need to complete a hands-on project to earn the certificate at the end of the specialization. The course contents are:

 

Course 1: The Data Scientist Toolbox

This module comprises introductory content to cover the concepts of a data scientist toolbox’s primary tools and ideas. In addition, the module provides an overview of how to use the tools for turning data into actionable knowledge and the fundamentals of how these tools can be used in programs such as version control, markdown, GitHub, and R and RStudio.

 

Course 2: R Programming

The second module consists of a complete introduction to R programming, starting with the fundamentals to high-level statistical programming requirements. The learners will also understand how to install and configure the software necessary to set up the statistical programming environment, including accessing the R packages, R functions, debugging, profiling R codes, and organizing them. In addition, there will be hands-on experience on the examples provided in this module.

 

Course 3: Getting and Cleaning Data

Learners need to understand how to acquire and clean the data before moving towards working with data. Therefore, this module focuses entirely on the basics of data and how the data can be obtained from the web, from APIs, from databases in various formats. Moreover, the learners will understand data cleaning and how it can lead to speedier downstream data analysis tasks. At the same time, the module also introduces essential topics such as working with raw data, processing instructions, codebooks, and how to work with processed data.

 

Course 4: Exploratory Data Analysis

This module provides learners an understanding of exploratory techniques for summarizing data. In addition, the learners will gain knowledge about applying these techniques for the formal modeling of complex statistical models.

Furthermore, there are advanced topics on eliminating and sharpening the potential hypothesis about real-world problems and how data can address them. Finally, the module covers plotting systems in R with the basics of constructing data graphics. Besides, the multivariate statistical techniques for the visualization of high-dimensional data are also covered in depth.

 

Course 5: Reproducible Research

The final module includes concepts and tools for reporting data appropriately, the concept of reproducible research for data analysis, and the scientific claims that data and coding can prove to verify the findings.

The learners will understand the importance of working with a large data set and how reproducibility is essential for sophisticated computational requirements. In addition, the learners will learn to differentiate between actual content-related data analysis versus superficial details. Additionally, the statistical analytical tools are covered with hands-on sessions to understand how to publish analytical data reports in a single document.

Instructor: Jeff Leek, Brian Caffo, and Roger D. Peng

Level: Beginner

Video Lectures: NA

User Review: 4.6/5

Price: Free Enrollment (Additional charges for certification may apply)

2. Data Analysis with R Programming by Google – Coursera

Data Analysis with R Programming by Google – Coursera

This course is a part of the Google Data Analytics course but is available for enrollment separately. It is available on Coursera. The course is a beginner-level course with no prior programming experience required for the learners.

This course will equip learners with the skills required for data analyst positions and have complete knowledge of statistical methods and programming. All the essentials of R programming and RStudio, including the R packages and R functions, are covered.

Moreover, this course provides the necessary foundations to efficiently clean data, organize, analyze, visualize, and report data in a newer and more robust approach. The industry engineers from Google data analytics teams will support the hands-on experience required for the course to learn how to accomplish data analysis with the best tools and resources offered by the R programming environment. At the end of the course, the learners will gain mastery over:

  • Examining and using the R programming language.
  • Applying R programming using RStudio for performing statistical analysis.
  • Complete understanding of the fundamental concepts with R programming.
  • Explore the contents and components of R packages and have a working knowledge about the Tidyverse package.
  • Understand how to work with data frames and their uses in R.
  • General Visualization using R
  • R Markdown for documenting R programming.

Instructor: Google Analysts

Level: Beginner

Video Lectures: NA

User Review: 4.8/5

Price: Free Enrollment (Additional charges for certification may apply)

3. Data Science: R Basics by Harvard University – edX

Data Science- R Basics by Harvard University – edX

The course is available on edX. This course introduces R programming basics and the key topics to perform practical data analysis using statistical methods in the R programming environment. R functions and data types are covered in-depth, and the learners will understand how to operate on vectors and advanced functions such as sorting.

The basics features of programming will be covered to understand how to wrangle, analyze and perform data visualization. To achieve an advanced level career in data science or data analysis requires solid foundations, which is the primary aim of the course. The course moves from the fundamentals to the advanced topics such as probability, inference, regression, and machine learning for data analysis.

The program helps learners narrow the steep learning curve with essential techniques taught with hands-on experience. The learners can develop a skill set that includes R programming skills, data wrangling using dplyr, data visualization with ggplot2, and file organization with UNIX and LINUX. In addition, the learners will also be introduced to version control with git and GitHub and learn to create reproducible documents using RStudio.

Instructor: Rafael Irizarry

Level: Beginner

Video Lectures: NA

User Review: NA

Price: Free Enrollment (Additional charges for certification may apply)

4. R Programming Fundamentals by Stanford – edX

R Programming Fundamentals by Stanford – edX

The course is available on the edX platform. It is an introductory course on R that covers the fundamentals from installation to the essential statistical functions. In addition, the learners will gain a working knowledge of variables, external data sets, and writing functions.

The takeaways from the course include the knowledge of using R in an interactive and easy-to-understand environment, data manipulations using R, uses of objects and storage, data structures, including data frames, lists, and matrices. In addition, the learners will understand how to import data and save the work in an R environment, plotting the data using ggplot2, preprocessing data and working with missing values, and the complete understanding of R packages.

Instructor: Susan Holmes

Level: Beginner

Video Lectures: NA

User Review: NA

Price: Free Enrollment (Additional charges for certification may apply)

5. Applied Data Science with R Specialization by IBM – Coursera

Applied Data Science with R Specialization by IBM – Coursera

This is a beginner-oriented specialization course available on Coursera. It is highly rated by the learners and is a comprehensive course for building strong R programming skills. This course includes five modules that cover crucial areas of R programming and data analysis. The learners will understand how to work with data sources and use R programming to transform the data into insights for better decision-making.

In addition, the learners will gain a comprehensive understanding of data preparation, statistical analysis, predictive modeling and creating relational databases, and querying the data with the help of SQL and R to communicate the data findings using data visualization techniques in R. In this specialization course, the learners will have complete hands-on lab sessions to gain the practical experience to work with various data sources, datasets, SQL, relational database and R programming in general.

The tools such as RStudio, Jupyter Notebooks, and R libraries for data science, including dplyr, Tidyverse, R Shiny, ggplot2, Leaflet, and rvest are covered in depth. The course modules are:

 

Course 1: Introduction to R Programming for Data Science

This module introduces the basics of R language and core components such as data types, techniques for data manipulation, and the implementation of fundamental programming tasks. The learners will learn about data structures and the relevant programming skills with the help of hands-on and practical learning, beginning with simple programs to manipulate data in a data frame or matrix.

 

Course 2: SQL for Data Science with R

The second module aims to introduce relational database fundamentals and apply the knowledge of SQL and R language in a data science environment. There is a strong emphasis on hands-on practice for real-world data science tools to work with real-world datasets. Additionally, the learners will learn to create a database instance in the cloud environment. Finally, a series of practical sessions will cover concepts on running SQL queries, accessing databases from Jupyter notebook with the help of SQL and R.

 

Course 3: Data Analysis with R

This module is associated with learning the R programming language for efficient data analysis. The module provides the learners a walk-through with concepts and techniques for data wrangling and a better understanding of data exploratory techniques for summarizing the data and identifying the relationships between different variables for better insights.

Upon completing the exploratory concepts, the learners will understand the development process for statistical model building and evaluating data and fine-tune its performance. Finally, there will be hands-on sessions using real-world data to create a model from scratches such as reading data, preprocessing, creating models, and improving their performance and evaluation.

 

Course 4: Data Visualization with R

In this module, the learners will understand graphical representations using R. The learners will explore how to build graphs and the uses of the ggplot2 data visualization package. In addition, the learners will apply this package to understand how it helps to create bar charts, histograms, pie charts, scatter plots, and line plots to visualize data. Besides, the customization of the charts and plots using themes and other visualization techniques is covered in detail. Furthermore, the learners will learn to use the Leaflet package in R to create map plots and other unique ways to plot data depending on the geolocation of the data.

Finally, the module covers the fundamental concepts of creating interactive dashboards with the help of the R Shiny package and understands how to customize apps that are created using Shiny and alter the appearance of the application with the use of HTML image components. Additionally, the learners will understand the process of deploying these interactive data applications on the web.

 

Course 5: Data Science with R Capstone Project

The final module includes the capstone project for achieving the IBM data science specialization certificate and the IBM badge. For this project, the learners will be provided with a pre-defined topic, upon which the learners are expected to perform all the necessary concepts that have been covered in the course and culminate with a data analysis report on the problem assigned for the project.

Instructor: Yan Luo, Rav Ahuja, Tiffany Zhu, Yiwen Li, Gabriela de Queiroz, Saishruthi Swaminathan, and Jeff Grossman

Level: Beginner

Video Lectures: NA

User Review: 4.9/5

Price: Free Enrollment (Additional charges for certification may apply)

6. Programming with Data Science with R – Udacity

Programming with Data Science with R – Udacity

This course is a nanodegree program offered on Udacity. Nanodegree programs are equivalent to a specialization program on other platforms. This course will teach the fundamentals of data programming tools such as R, SQL, command line, and git. There are no prerequisites for the course.

Additionally, the learners will be working on real-world projects and cover essential data science and operations topics. Besides, there is a provision for technical mentor support, project reviews, and feedback from industry experts. The course curriculum includes:

 

Introduction to SQL

This module covers SQL fundamentals such as JOIN, aggregation, subqueries, and important database-related queries to work with complex business problems.

 

Introduction to R Programming

The second module covers R programming fundamentals such as data structures, variables, loops, and functions and applying essential data exploratory techniques. In addition, the learners will gain insight on how to use a data visualization library such as ggplot2.

 

Introduction to Version Control

The final module covers the concepts on version control and how to use GitHub and post the work to communicate with industry people from the data science field.

Instructor: Josh Bernhard, Derek Steer, Juno Lee, Richard Kalehoff and Karl Krueger

Level: Beginner

Video Lectures: NA

User Review: 4.8/5

Price: Monthly Access- $314.7, 3-Month Access: $802.7 (Prices may vary as per the region)

7. Data Science with R Certification Course – Simplilearn

The data science with R certification course is available on Simplilearn. This online training course covers data exploration, data visualization, predictive analytics, descriptive analytics using R language. The learners will learn about the import and export of data, R packages, R data structure, and several statistical concepts alongside cluster analysis and forecasting. In addition, there are dedicated mentoring sessions that are offered with the courses and a total of ten real-life industry projects.

The takeaway from this course is a comprehensive understanding of business analytics,  R Programming and the packages, data structures, data visualization techniques, and the application of functions and dplyr function. In addition, the learners will have a thorough understanding of the graphical representation of data for analysis, hypothesis testing, Apriori algorithm, and k-means and DBSCAN clustering concepts.

The course curriculum includes:

  • Introduction to Business Analytics
  • Introduction to R Programming
  • Data Structures
  • Data Visualization
  • Math Refresher
  • Statistical Essentials for Data Science
  • Statistics for Data Science I and II
  • Regression Analysis
  • Classification
  • Clustering
  • Association

Instructor: Industry Professionals, Ronald Van Loon (Course Advisor)

Level: Beginner

Video Lectures: NA

User Review: 4.6/5

Price: Self-paced- $275.4, Live online classroom training- $303

8. Advanced Statistical Inference and Modelling Using R – edX

Advanced Statistical Inference and Modelling Using R – edX

This course is offered by the University of Canterbury, New Zealand, on the edX platform. The course is suitable for learners familiar with basic concepts on linear regression and the fundamentals of statistical inference. The learners will understand the use of linear regression for different situations, such as when the response variable is binary, count, categorical or hierarchical. The course provides a practice-oriented approach to understanding the R programming methods and the appropriate uses of these methods.

At the end of the course, the learners will understand data exploratory techniques, data visualization, multivariate analysis using Generalized Linear Models, mixed effects of linear regression models and structures. In addition, the learners will have clarity over concepts like diagnostics and interpretation, and model selection. The learners will also explore how to evaluate sample size and handling of missing data for improved analysis.

Instructor: Elena Moltchanova

Level: Advanced

Video Lectures: NA

User Review: NA

Price: $254.7

9. R Programming A-Z: R for Data Science with Real Exercises – Udemy

R Programming A-Z- R for Data Science with Real Exercises – Udemy

This course on R programming is available on Udemy. In general, R has a steep learning curve as it includes multiple disciplinary concepts. This tutorial provides a comprehensive understanding of R programming and is suitable for beginners looking to start from the absolute fundamentals.

At the end of the course, the learners will have a good understanding of R programming, the core principles, variables, loop concepts in R, matrix, and customization of RStudio as per the user’s preference. In addition, the concepts of normal distribution, creation of vectors using R programming, R packages, and its uses will be covered by building hands-on experience with multiple practical exercises. The course curriculum includes:

  • Basics of R and Installation
  • Core Programming Principles
  • Fundamentals of R
  • Matrices
  • Data Frames
  • Advanced visualization with ggplot2
  • Practical Sessions
  • Bonus Tutorials

Instructor: Kirill Eremenko and Ligency Team

Level: Beginner

Video Lectures: 82

User Review:4.6/5

Price: $5.3 (Charges may vary according to the region)

10. IBM Data Analytics with Excel and R Professional Certificate – Coursera

IBM Data Analytics with Excel and R Professional Certificate – Coursera

This professional certificate program by IBM is offered on the Coursera platform. It is intended for learners seeking job-ready skills for entry into the field of data science. This course is an 11-month program that deep dives into the concepts of data science and data analytics.

The learners will gain essential skills by mastering data sources, Excel, analytical tools, Cognos Analytics, and R programming. At the end of the course, the learners will have a complete understanding of data analysis, data visualization, and reporting using charts and plots, interactive dashboards, and practical knowledge of relational databases and SQL statements. In addition, the learners will have complete hands-on experience with R programming to perform the data analysis process such as data preparation, statistical analysis, data visualization, predictive modeling, and creating interactive data applications alongside report preparation for stakeholders. The course modules are:

  • Course 1: Introduction to Data Analytics
  • Course 2: Excel Basics for Data Analytics
  • Course 3: Data Visualization and Dashboards with Excel and Cognos
  • Course 4: Introduction to R Programming for Data Science
  • Course 5: SQL for Data Science with R
  • Course 6: Data Analysis with R
  • Course 7: Data Visualization with R
  • Course 8: Data Science with R Capstone Project

Instructor: Rav Ahuja, Sandip Saha Joy, Steve Ryan, Yan Luo, Yiwen Li, Tiffany Zhu, Gabriela De Querioz, Jeff Grossman, and Saishruthi Swaminathan

Level: Beginner

Video Lectures: NA

User Review:4.8/5

Price: Free Enrollment (Additional charges for certification may apply)

11. Data Science with R – Pluralsight

This course is available on Pluralsight. In this course, the learners will learn about data science and how the R programming language plays a significant part in dealing with data. The learners will understand the techniques for data transformation into insights.

In addition, the learners will gain knowledge about the concepts of cleaning data, creating and interpreting descriptive statistics, data visualization techniques, and building statistical models. Besides, the learners will be introduced to big data concepts and how to make predictions with the help of machine learning algorithms and the use of R programming language in the production environment. The course contents are:

  • Introduction to Data Science
  • Introduction to R
  • Working with Data
  • Creating Descriptive Statistics
  • Creating Data Visualization
  • Creating Statistical Models
  • Handling Big Data
  • Predicting with Machine Learning
  • Deploying to Production

Instructor: Matthew Renze

Level: Beginner

Video Lectures: NA

User Review:4.8/5

Price: 10-Day Free Trial (Charges applicable after trial period)

12. Introduction to Business Analytics with R by the University of Illinois – Coursera

Introduction to Business Analytics with R by the University of Illinois – Coursera

This course is available on Coursera. In this course, the learners will gain experience working knowledge of data analytics concepts and prepare business data using various analytical techniques and tools. In addition, the learners will understand the uses of machine learning algorithms, data visualization, and performing essential functions such as cleaning, transforming, aggregating, and preparing data using the R programming language.

The learners will also understand the functionalities of the RStudio environment and the built-in features. At the end of the course, the learners will understand the business analytic workflow and the various principles and data analysis techniques to process data. The learners will also work on industry-based business problems with the help of data automation and analytics and various methods for communicating the analytical results. The course curriculum includes:

  • Data Analytic Language to Solve Business Problems
  • Getting to know Data and Share It
  • Data Preparation Functions
  • Preprocessing Data

Instructor: Ronald Guymon and Ashish Khandelwal

Level: Beginner

Video Lectures: NA

User Review:4.5/5

Price: Free Enrollment (Additional charges for certification may apply)

Conclusion

In the era of digitization, data holds the key to crucial business decisions. The field of data science and big data involves working with a large amount of data to gain business insights for fulfilling the operational goals of the business organizations. Therefore, significant companies are looking for data scientists, data analysts, and other relevant job roles. However, most companies prefer engineers and statisticians, including data engineers who can work well with data using analytical techniques and tools and be equipped with R programming skills to better present data for in-depth analysis.

In this context, it becomes essential for aspirants and experienced personnel to upskill themselves with the right theoretical and programming skills associated with R. To achieve that, one must opt for the suitable online courses that can provide the necessary exposure to real-life industry projects and build the programming skills alongside the theoretical understanding of the concepts.

In addition, the certification should be industry-recognized to add value to the career prospects and gain a competitive advantage in the market. Thus, this article highlighted some of the top courses on R programming language.

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Kaushik Das

Kaushik is an artificial intelligence researcher and a data scientist with expertise in medical image processing, intelligent automation, computer vision, and big data engineering—a technical and a scientific writer by passion. He is also passionate about sports, photography, traveling, and exploring new technologies.

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