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R Programming Certificate Course

  • Online and Offline Classes
  • Certificate of Completion
  • 100% Placement assistance
  • Industry focused curriculum
  • Timely assignments
  • Mock interviews

Learn how to manipulate data, create visualizations, and make data-driven decisions with our comprehensive R programming course

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    "Master Data Analytics with R Programming: Unlock the Power of Statistical Computing"

    R programming language is rather an environment for statistical computing and data science. Having its application on a global scale, R programming language will open a world of opportunities to you. We introduce you to programming fundamental for R, data structures to use, and working with data in R using different data formats. You will also be working with data visualization techniques.

    Problem solving skills

    Design fundamentals

    Career advancement

    Industry best practices

    Career Opportunities

    Discover and unleash the potential of your future by exploring the plethora of exciting career opportunities available to you

    Research Analyst

    Data Visualization Specialist

    Quantitative Analyst

    Statistical Analyst

    Business Intelligence Analyst

    Discover the Fundamentals of Development with Our Comprehensive Course!


    In-depth training with hands-on application


    One-on-one mentorship with industry experts


    Real-world projects for practical learning


    Theory classes are followed by practical sessions conducted in labs


    Our curriculum is tailored to provide you with a comprehensive understanding of R programming
    • R Basics

      • Math, variables and strings
      • Vector operations
      • Introduction R nuts and bolts

    • Data Structures in R

      • Arrays and Matrices
      • Lists
      • Dataframes

    Features of the course

    4 months curriculum

    Get a robust 4-month curriculum that covers all the essentials of the course

    Unlimited Mentoring

    Get mentoring from industry experts to guide you every step of the way

    Build Your Portfolio

    Build an impressive portfolio of real-world projects to showcase your skills and stand out from the competition

    Placement Support

    Get dedicated placement support to help you land a job and kickstart your career

    A student’s journey

    Shape Your Career with Our Comprehensive Journey

    Select Project

    Choose your project topic and start the project with our project-based learning approach

    Complete UI and Create Database

    Complete the frontend of your project and prepare its database

    Complete Backend

    Create API’s required for your project

    Get your Project reveiwed

    After completing the project, get it approved by your mentor

    Upload to Github

    Upload your created project on Github

    1st project Completed

    After uploading, present your project to your mentors and your 1st project will be completed!

    Student Work

    Our student work showcases the real-world application of the skills and techniques you will learn in our course. Get inspired and see what you can achieve with our program

    Our recent placements

    Our placement and success stories showcase the achievements of our students and the opportunities that are available

    Ria Deshmukh
    Engineer (Pre Felix)
    UI/UX Designer (Post Felix)
    Working at
    Prashanti Nagdeve
    Event Manager (Pre Felix)
    UI/UX Designer (Post Felix)
    Working at
    Swati Lodha
    Fresher (Pre Felix)
    Full Stack (Post Felix)
    Working at

    We are proud to have trained professionals who have gone to work at some of the biggest names in the industry

    Course Completion Certificate

    Award Yourself for Your Hard Work

    Proudly display your certificate and show the world what you have accomplished with our program.


    If you meet the below criteria, you are eligible to join our comprehensive R Programming course and start your journey to success!"

    • IT PROFESSIONALS If you are a professional and looking to enhance your profile then this course is the right choice as a first step in upscaling your profile.
    • ENTRY LEVEL DEVELOPERS Fresh graduates who are looking to make an entry in IT world this course would be good start to make yourself stand apart from the crowd ,get your fundamentals strong.

    Where to begin your journey?


    Attend our free webinar

    Get an introduction to the world of R Programming and learn how it can benefit your career. Our expert instructors will guide you through the basics.



    Live session with mentor

    Take your learning to the next level with a one-on-one session with a professional R Programming Specialist.

    Book session


    Enroll for the course

    Sign up now and get ready to embark on your R programming journey with confidence and support!"


    New Batch Alert

    Are you ready to start your R programming journey? Our next batch of the comprehensive R programming course is starting soon. Join a community of like-minded individuals and learn from industry experts.

    27 June

    4:30 pm to 6 pm


    *Limited seats

    Want to know other batch availability?


    It allows the integration with other languages like C, C++ and Java. Thereby, it enables you to communicate with many data sources. This course is applicable across various computing platforms and is in demands in the current age of digitization.

    The course specifically has no requisites as we work on students right from grassroot levels. Anyone with basic Personal computing expertise is ready in our opinion to take this course.

    This course spans over a period of 45 days, including of your projects and assessments.

    Job opportunities that open up are that of an applied scientist, analysts (knowledge and statistical), and computer code developer.

    This is a course that has been overseen by international standards of precision and accuracy and hence is acknowledged worldwide.

    R Programming Basics

    1. What is R programming used for?

    R is a computer language that is free and open-source and is used for statistical computation and graphics. Statisticians, data scientists, and analysts use it extensively to undertake data analysis, produce visualisations, and design statistical models. R is also utilised in other sectors such as finance, economics, biology, and medicine. Studying an R programming course can help you gain a better understanding of this programming language.

    2. How do I install packages in R?

    There are two main ways to install packages in R: using the install.packages() function or the graphical user interface (GUI) in an R IDE like RStudio. With the install.packages() function, specify the package name in parentheses. In the GUI, go to the "Tools" menu, select "Install Packages," enter the package name, and click "Install." An R programming course helps students understand these critical functionalities.

    3. What is the difference between a data frame and a matrix in R?

    In R, matrices and data frames are both two-dimensional data structures, but they differ in terms of data types they can store. Matrices can only contain one data type, such as numeric or character, while data frames can store multiple data types in different columns. Data frames are more versatile and commonly used for data analysis and manipulation.

    4. How do I load data from a CSV file into R?

    To load data from a CSV file into R, you can use the read.csv() function. Simply specify the file path and any additional options, such as whether to include headers or convert character variables to factors. The data will be loaded into a data frame, which is a tabular structure for organizing and analyzing data.

    5. How do I handle missing values in R?

    Missing values, represented by NA in R, can be handled using various methods. One way is to simply remove rows or columns containing missing values. Another approach is to impute missing values by replacing them with estimates based on other data points. Common imputation techniques include mean, median, or mode imputation.

    6. What are factors in R and how do I work with them?

    In R, factors hold different categories of data. They're used to manage categorical variables, which have a limited set of possible values, like "male" or "female" for gender or "yes" or "no" for a question. You can work with factors by accessing their levels, labels, and order using the factor() method. Factors are useful for statistical analysis because they ensure that R correctly handles categorical data.

    7. How do I perform basic statistical calculations in R?

    Performing basic statistical calculations in R is simple. You can use built-in functions to calculate measures like mean, median, mode, standard deviation, and range. For example, to calculate the mean of a vector named 'data', you would use the function mean(data). Similarly, you can use the median(data) for the median, mode(data) for the mode, sd(data) for the standard deviation, and range(data) for the range.

    8. What is the difference between the base plot system and ggplot2 in R?

    The base plot system in R is the original plotting system, while ggplot2 is a more recent and widely used system. This system is powerful and flexible, but it can also be difficult to use.Ggplot2 is more flexible and easier to use than the base plot system, and it produces more attractive and publication-quality plots. It is also more flexible than the base plot system, and it can be used to create a wider variety of plots.

    9. How do I write a loop in R to repeat a set of instructions?

    In R, you use the for keyword followed by parentheses containing the control variable, the starting and ending values separated by a colon, and the code block to be repeated within curly braces to create a loop. The loop iterates across the provided range of values, assigning each value in turn to the control variable. In each iteration, the code block is run.

    10. What are the best practices for organizing and documenting R code?

    Here are some best practices for organizing and documenting R code:
    Use descriptive file and function names:This will make it easier for you and others to find the code you need.
    Use comments to explain your code: It is important for complex code or code that you may not come back to for a while.
    Break your code into smaller chunks:This will make it easier to read and debug.
    Use a consistent coding style: It will make your code more readable and easier to maintain.
    Document your code with Roxygen: Roxygen is a system for documenting R code that is similar to Javadoc.
    Use a version control system: This will allow you to track changes to your code and revert to previous versions if necessary.