Data Science Job Seekers: Best R Programming Certifications You Absolutely Need

Companies all around the globe are struggling to manage the massive volumes of data at their disposal, and an even greater difficulty is figuring out how to manage future datasets that will be exponentially larger. The following statistics demonstrate the growing demand for data science skills and the lucrative opportunities available in this field.

• According to LinkedIn, one of the top 15 emerging jobs in the U.S. is that of a data scientist with demand for data science professionals growing at an annual growth rate of 37%.
• A World Economic Forum (WEF) report predicts that data-related jobs, including that of data scientists, will be among the most in-demand roles globally by 2026.
• The U.S. Bureau of Labor Statistics (BLS) predicts that employment of computer and information research scientists, which includes data scientists, will grow by 19% between 2020 and 2030, much faster than the average for all occupations.
• A report by Burning Glass Technologies found that the demand for data science skills is growing faster than the supply of talent, with a projected shortage of 250,000 data scientists in the United States alone by 2024.

According to Glassdoor, the national average salary for a data scientist in the United States is $113,309 per year, with some top-paying companies offering salaries well over $200,000 per year.  

The following infographics illustrate the average salary of data scientists in USA:

The following infographics illustrate the average salary of data scientists in India:

The following infographics illustrate the average salary of data scientists in the U.K:

Professionals and students with no connection to computer science, engineering, mathematics/statistics, or general science cannot work in this field. This is because, data science is an interdisciplinary field that needs expertise in one or more of these fields.

Further, being a data science expert needs far more than a basic understanding of programming or coding. You must be skilled in the use of technologies such as Spark, Hadoop, and NoSQL. It is also important to be well-versed in machine learning, programming, and statistical modeling. Knowledge of the R programming language is also a great advantage. If you are looking to break into the data science industry, it is important to know how to learn R programming and adding it to your skill set.

In fact, learning R programming can be a game-changer for your career. It is one of the top skills in demand in the data science industry. It is a popular programming language used for statistical computing and graphics, and can help in data analysis, data visualization, and data manipulation. With the right expertise and knowledge, you can take advantage of the growing opportunities in this field and build a successful career.

R is a programming language developed by University of Auckland faculty members Ross Ihaka and Robert Gentleman. It is a statistical programming language that is commonly used by data scientists and business executives to understand data.

Numerous computer languages may be used for data science processes, but R makes data assessment and collection fun and unique. R is a sophisticated programming language capable of doing a broad variety of complex statistical computations and calculations. As a result, it is widely used by data scientists and business leaders across a wide range of industries, from academia to industry.

Additionally, R graphically interprets the data, making it easy to study and comprehend. R also offers a number of options for advanced data analysis techniques such as machine learning, algorithms, and so on. Lastly, R makes it simple to do many data science operations since it excels at data wrangling, data visualization, statistical computations without vectors, and web applications, among other things, allowing you to swiftly collect and analyze massive quantities of data.

Mastering R programming skills are essential for a career in data science. According to recent reports, R is one of the top 10 recession-proof data science skills to master in 2023. By mastering R programming, data scientists can efficiently manipulate, analyze, and visualize large sets of data, making it a valuable skill to have in today's job market. Additionally, learning R can help you stay up to date with the latest tools and techniques in data science, enabling you to build a successful career in this field.

Let us take a deeper look at the R programming language's working mechanism, foundations, and features now that we've shown that R is the most powerful computer language for data research.

R is a programming language that is frequently used in data research and machine learning. This programming language was invented in the 1900s and has gained popularity among data scientists in the last decade. When employed in data analysis, R yields intriguing outcomes - powerful analytics, fascinating visualizations, exact statistics, and so on. These distinctive characteristics make it a popular option among data scientists and business leaders.

Characteristics of R 

• R is free software that anybody can use. Additionally, the programming language is versatile, making integration with other applications and procedures simple. Despite the fact that it is free and open-source software, the programming language emanates quality and is both useful and adaptable.
• Powerful graphics is one of the appealing aspects of R programming. R is used by many data scientists for data analysis because it offers static graphics that provide high-quality data visualizations. Moreover, the programming language has a large library that includes interactive visuals and makes data visualization and representation simple to understand.
• R includes everything that makes data analysis intriguing and simple, from elaborative and interactive flow diagrams to bar graphs.
• R creates its own community. Since it is an open-source computer system that fosters a feeling of community among its users, the programming language is extensively utilized by data scientists and business executives all over the world.
• R can be used to do basic and complicated mathematical and statistical calculations.
• R is interoperable with computer languages such as C, C++, Java, Python, and others. Its functionalities are simple to include into many computer applications.

These and other qualities make R a popular option among data scientists. Surprisingly, R is used in data research by web apps such as Twitter, Google Analytics, BBC, and others. They utilize it to collect data and develop understandable data visualizations or graphs from which to derive conclusions and implement business decisions. 

Using R 

R is free to download and install since it is open-source software. The program may be downloaded and installed from the Comprehensive R Archive Network (CRAN) website. It may be quickly and easily installed in your operating system. You may also install RStudio, a package that simplifies R programming.

Don't forget to download add-on packages, which are pre-assembled collections of functions and objects that make running R easier and less time-consuming. Popular add-on packages include DBI, RMySQL, and RSQLite. Each package is simply available on the CRAN repository and may be downloaded.

R Analytics 

It refers to the process of analyzing data using R programming. For analyzing data, business executives and data science pioneers choose to utilize the R programming language because it generates error-free graphs and visuals. It is flawless in every way and is ready for publication right away. Furthermore, R is well-known for its visualizations, and we all know that drawings and graphs are simpler to understand for the average layperson. As a result, R is widely used in data science and data analysis.

Below-mentioned are some of the best R programming course online that can help upgrade your skills:

Parallel Programming in R

• Overview
  This course teaches parallel computing capabilities available in R in-depth. It covers the principles and tools available in R for parallel computing, as well as answers to a few essential non-trivial parallel processing concerns such as reproducibility, random number generation, and load balancing.
   
• Course Content
  In order to benefit from the parallel environment, the application must be divided into portions. The introductory chapter will teach you about partitioning and how it relates to different hardware configurations. You will also learn about other R packages that assist parallel programming. The parallel package chapter deals with the numerous backends and their variations, as well as a thorough grasp of the package's workhorse, the clusterApply() function. Methods for task fragmentation, as well as their difficulties, are also explained.
  
  Foreach, FutureApply and Load Balancing chapter will go over two user-contributed packages that make parallel programming in R even easier. They are constructed on top of the packages parallel and future. The final lesson in this chapter will teach you about the benefits and drawbacks of load balancing and scheduling.
  
  Can I duplicate my results if the program uses random numbers? Can I get the same results whether the code runs sequentially or in parallel? The Random Numbers and Reproducibility chapter will provide answers to these questions. You will learn about a parallel-optimized random number generator and how it is used by various packages. The best thing is that this training is completely free of charge.
   
• Career Opportunities Post Completion
  After studying parallel programming, you can qualify for a variety of job roles that require expertise in this field. Some potential career paths include:
  • Parallel Computing Software Developer: You can work on developing software that uses parallel processing techniques to speed up computations. The average salary for this role is around $100,000 to $125,000 per year.
     
  • High-Performance Computing Engineer: You can work on designing and implementing high-performance computing systems that leverage parallel processing to achieve faster processing times. The average salary for this role is around $120,000 to $150,000 per year.
     
  • Data Scientist: You can apply your knowledge of parallel programming to develop and optimize algorithms for processing large datasets. The average salary for this role is around $105,000 to $130,000 per year.
     
  • Researcher: You can work in academia or research institutions to study and improve parallel programming techniques and algorithms. The average salary for this role is around $90,000 to $110,000 per year.
     
  • Cloud Computing Engineer:  You can work on developing and implementing cloud-based computing systems that use parallel processing to increase efficiency. The average salary for this role is around $115,000 to $140,000 per year.
     
  • Video Game Developer: You can apply your knowledge of parallel programming to develop high-performance gaming engines that can handle complex graphics and physics calculations. The average salary for this role is around $85,000 to $110,000 per year.
     
  • Computer System Analyst: You can work on analyzing existing computing systems and identifying areas where parallel processing can be used to improve efficiency and speed. The average salary for this role is around $80,000 to $100,000 per year.

Overall, the job opportunities after studying parallel programming are diverse and exciting, and the demand for experts in this field is expected to grow as computing systems become increasingly complex and large-scale.

Defensive R Programming

• Overview
  In this course, we teach defensive programming, which is a collection of common approaches for reducing defects and working in teams. We look at ways to prevent frequent faults, as well as how to deal with the inevitable error that may occur in our code. The course ends after teaching students when to shift from script to project to package.
   
• Course Content
  In the first chapter, ‘Avoiding Conflict’, you will discover what defensive programming is and how to make better use of existing packages. You will next learn how to manage the packages loaded in your environment, as well as any potential conflicts.
  
  When you get feedback on your code execution, programming becomes easier. To keep the user informed, we use messages, warnings, and errors in R. In the ‘Early Warning Systems’ chapter will go through when and when various communication techniques should be used. By employing a consistent programming strategy, we can prevent making mistakes. We will expose you to R best practices in ‘Preparing your Defenses’ chapter.
  
  Writing a script is fun, but working on a project with several scripts and materials necessitates organization. The last chapter ‘Building a Battle Plan’, will show you how to organize your work so that you can move from script to package with an efficient process. The best thing is that this course is completely free.
   
• Career Opportunities Post Course Completion
  After studying Defensive R Programming, you can qualify for various job roles in the field of data science and programming, including:
  • Data Analyst: You can work with data to identify and solve problems using R programming. Your knowledge of defensive programming techniques can help you write more robust code, improving the reliability of data analyses. The average salary for this role is around $75,000 to $90,000 per year.
     
  • Software Developer: You can apply your knowledge of defensive programming in developing software applications that are more secure and reliable. The average salary for this role is around $95,000 to $110,000 per year.
     
  • Data Scientist: You can work on developing models and algorithms to analyze data using R programming while applying defensive programming techniques to ensure the quality and reliability of the results. The average salary for this role is $105,000 to $130,000 per year.
     
  • Quality Assurance Analyst: Your knowledge of defensive programming can help you develop and execute test plans to ensure the reliability and accuracy of software applications. The average salary for this role is around $70,000 to $85,000 per year.
     
  • R Package Developer:  You can use your knowledge of defensive programming to develop packages in R that are more robust and reliable. The average salary for this role is around $90,000 to $110,000 per year.
     
  • Technical Writer: You can write technical documentation for R packages and applications, using your knowledge of defensive programming to help improve the quality and reliability of the documentation. The average salary for this role is around $60,000 to $75,000 per year.
     
  • Data Engineer: You can work on developing and maintaining data pipelines using R programming, applying defensive programming techniques to ensure the quality and reliability of the data. The average salary for this role is around $100,000 to $125,000 per year.

Overall, the job opportunities after doing this course are immense. Your knowledge of defensive programming techniques can help you stand out in the job market and ensure the reliability and quality of your work.

Data Analytics with R Programming Certification Training

• Overview
  Data analytics with R course is one of the best R certification that will help you gain competence in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis, and utilizing R Studio for real-world case studies on Retail and Social Media.
   
• Course Content
  The online ‘Data Analytics with R Certification’ course from Edureka teaches students how to use the R programming language. The training includes statistical analysis and modeling. Edureka's classes teach vital skills such as organizing and organizing data sets and showing findings in a visually attractive manner. This one-of-a-kind series of interactive lectures will teach students how to use techniques like regression and model-building to make smarter decisions at work. Moreover, it also covers:
  • Introduction to Data Analytics
  • Introduction to R Programming
  • Data Manipulation in R
  • Data Import Techniques in R
  • Exploratory Data Analysis
  • Data Visualization in R
  • Data Mining: Clustering Techniques
  • Data Mining: Association Rule Mining & Collaborative filtering
  • Linear and Logistic Regression
  • Anova and Sentiment Analysis
  • Data Mining: Decision Trees and Random Forest
    
    It has very good hands-on training and a user rating of 4.5 out of 5 stars. Moreover, this self-paced course conducted by IIT academicians and industry experts is suitable for beginners as well. The course price is ₹16,995. However, students also can avail the no-cost EMI starting at ₹5,665 per month. This course is worth investing in if a learner is serious about becoming a data analyst or working with statistical inference and emotional analysis.
     
• Career Opportunities Post Course Completion
  After completing Data Analytics with R Programming Certification Training, you can qualify for various job roles in the field of data science and analytics, including:
  • Data Analyst: You can work with data to identify and solve problems using R programming. Your knowledge of data analytics techniques can help you provide insights and recommendations to stakeholders. The average salary for this role is around $75,000 to $90,000 per year.
     
  • Business Analyst: You can analyze data to identify trends and provide recommendations for business decisions. Your knowledge of R programming can help you manipulate and visualize data to communicate insights. The average salary for this role is around $80,000 to $95,000 per year.
     
  • Data Scientist: You can work on developing models and algorithms to analyze data using R programming. Your knowledge of data analytics techniques can help you identify patterns and make predictions. The average salary for this role is around $105,000 to $130,000 per year.
     
  • Marketing Analyst: You can analyze customer data to help inform marketing strategies and campaigns. Your knowledge of R programming can help you analyze large datasets and communicate insights to stakeholders. The average salary for this role is around $70,000 to $85,000 per year.
     
  • Financial Analyst:  You can analyze financial data to help inform investment decisions. Your knowledge of R programming can help you create models and visualize data to communicate insights to stakeholders. The average salary for this role is around $75,000 to $95,000 per year.
     
  • Database Administrator: You can work on developing and maintaining databases, including managing data and ensuring data quality. Your knowledge of R programming can help you manipulate and analyze data to identify issues and improve data quality. The average salary for this role is around $85,000 to $110,000 per year.
     
  • Machine Learning Engineer: You can work on developing and deploying machine learning models using R programming. Your knowledge of data analytics techniques can help you identify patterns and create accurate models. The average salary for this role is around $110,000 to $150,000 per year.

Overall, the job opportunities after completing Data Analytics with R Programming certification training are diverse. Your knowledge of R programming and data analytics techniques can help you stand out in the job market and ensure the quality and reliability of your work.

Data Science on Microsoft Azure using R Programming

• Overview
  Learn about data science on Microsoft Azure, improve your data science abilities, and master R programming.
   
• Course Content
  There are 6 courses under this certification R programming course. The total duration of all the courses is 18 weeks and the bundle price is $39 per month or approximately ₹3,222. Their description is as follows:
  • Course 1: Microsoft Future Ready: Introduction to Azure Cloud for Data Scientists
    This course will help to improve your grasp of cloud services by learning Azure and Amazon Web Service.
     
  • Course 2: Microsoft Future Ready: Data Query with Transact-SQL (T-SQL)
    This course will assist in the development of Transact-SQL skills, which are required for data professionals and developers who work with SQL databases.
     
  • Course 3: Microsoft Future Ready: Introduction to Data Science
    This course is intended to be an advanced continuation of the Introduction to Data Science course.
     
  • Course 4: Microsoft Future Ready: Fundamentals to R for Data Science
    R is quickly becoming the most popular programming language in data science and statistics, and this course will teach you how to use it.
     
  • Course 6: Microsoft Future Ready
    This course will assist you in creating and implementing a Data Science Solution on Azure.
     
• Career Opportunities Post Course Completion
  After completing this R coding certification: Data Science on Microsoft Azure using R Programming, you can qualify for various job roles in the field of data science and analytics, including:
  • Data Scientist: You can work on developing models and algorithms to analyze data using R programming and Azure tools. Your knowledge of data science techniques can help you identify patterns and make predictions. The average salary for this role is around $100,000 to $130,000 per year.
     
  • Data Engineer: You can work on designing, building, and managing data pipelines using Azure tools and R programming. Your knowledge of data science and engineering techniques can help you ensure data quality and optimize data processing. The average salary for this role is around $90,000 to $120,000 per year.
     
  • Business Analyst: You can analyze data to identify trends and provide recommendations for business decisions. Your knowledge of Azure tools and R programming can help you manipulate and visualize data to communicate insights. The average salary for this role is around $70,000 to $100,000 per year.
     
  • Machine Learning Engineer: You can work on developing and deploying machine learning models using R programming and Azure tools. Your knowledge of data science and engineering techniques can help you create accurate models. The average salary for this role is around $110,000 to $150,000 per year.
     
  • Cloud Solution Architect:  You can work on designing and implementing cloud-based solutions using Azure tools and R programming. Your knowledge of data science and engineering techniques can help you ensure scalability and performance. The average salary for this role is around $130,000 to $160,000 per year.

Overall, the job opportunities after completing Data Science on Microsoft Azure using R Programming are diverse and offer a wide range of career paths in data science and analytics.

Managing Big Data with R and Hadoop

• Overview
  This course will provide you with access to a virtual environment with Hadoop, R, and Rstudio installations so that you may gain hands-on experience with big data management. For testing and learning, several unique examples from statistical learning and accompanying R code for map-reduce operations will be accessible.
  
  Individuals having a rudimentary understanding of statistical learning and R will be able to better comprehend the approaches and how to run them in parallel utilizing map-reduce functions and Hadoop data storage. You will get access to RHadoop on a supercomputer at the University of Ljubljana at the end of the course.
  
  We anticipate that the learners will also have basic expertise with Linux and bash, as well as working knowledge of R and matrix operations. They should be able to download and run virtual machines as well. Every software required to fully engage in the course is given within the virtual system, which followers must download and install on their home PC. There is no additional software required. A simple local system with 15GB free disc space and 2GB RAM is required.
   
• Course Content
  The course duration is 5 weeks, and course fee is $16 or approximately ₹1322 per month. The course curriculum covers the following topics:
  • Welcome to BIG DATA
  • Working with Hadoop
  • First steps in R and RHadoop
  • Statistical learning with RHadoop: clustering
  • Statistical learning with RHadoop: regression and classification
     
• Career Opportunities Post Course Completion
  After completing a course in managing big data with R and Hadoop, you can qualify for various job roles in the field of big data and analytics, including:
  • Big Data Engineer: You can work on designing, building, and managing big data systems using Hadoop and R. Your knowledge of data science and engineering techniques can help you ensure data quality and optimize data processing. The average salary for this role is around $100,000 to $140,000 per year.
     
  • Data Scientist: You can work on developing models and algorithms to analyze big data using R and Hadoop. Your knowledge of data science techniques can help you identify patterns and make predictions. The average salary for this role is around $95,000 to $130,000 per year.
     
  • Business Analyst: You can analyze big data to identify trends and provide recommendations for business decisions. Your knowledge of Hadoop and R can help you manipulate and visualize big data to communicate insights. The average salary for this role is $70,000 to $100,000 per year.
     
  • Big Data Architect: You can work on designing and implementing big data solutions using Hadoop and R. Your knowledge of data science and engineering techniques can help you ensure scalability and performance. The average salary for this role is around $130,000 to $160,000 per year.

Overall, the job opportunities after completing Managing Big Data with R and Hadoop and for that matter in general R certifications are diverse.

In conclusion, getting an R programming certification can greatly benefit your career growth in 2023. The demand for professionals with data science skills continues to rise, and having a certification in R programming can set you apart from other candidates and lead to a wide range of career opportunities. These days, a lot of leading course providers offer various R programming courses online that facilitate learning for working professionals. It allows exploring endless career opportunities from data analysts and statisticians to machine learning engineers and data scientists and much more. So don't hesitate to invest in yourself and take the first step towards mastering R programming and unlocking a successful career in the field of data science.