Level : Beginner

Getting Started with R

Statistical Programming and Data Analytics using R

Gain proficiency in fundamental data analysis in R, encompassing vectors, lists, and data frames, and apply R skills using actual datasets.

Extensive 40 Hours Professional 100 Percent Virtual Online Program

Training Duration

40 Hours

Session Type

Virtual Instructor-led Online (Microsoft Teams)

Projects/Case Studies


Forty Hours (40) of Curriculum

  • Introduction of R

    Overview of R, Business applications of it, Features of R Language Installation of R and R- Studio, Understanding R-Studio interface

  • Basic programing concepts

    Variables: Concept of Variables, Types of Variables Data Types: Integers, Double, Character, Logical Operators: Arithmetic, Relational and Logical

  • R Data structure

    Vector: Create Vector, Retrieve, Appending, Update, Delete Elements from it & other Functions; seq, rep, assigning names to elements, etc. Matrix: Working with Matrix and related functions

  • List: Handling List object in R

    Factor: Understanding Factor data, Levels in Factor type, scenarios of its usage Dataframe: Working with Dataframe, CRUD operations on it, merging and adding operations on Dataframes

  • Program Control Flow - Branching

    If - Else statements

  • Using Loops

    For Loop, While and Repeat loop

  • User defined functions

    Creating Functions with/ without parameters

  • R Built in functions

    Using various built-in function on numeric, character data types

  • Date type built in functions, using series of Apply functions

  • Debugging the code, understanding libraries

  • File Handling and Regular expressions

  • Handling data with dplyr package using filter, arrange, select, mutate, group by & summaries, pipe operator, etc.

  • Graphs & Data Visualization techniques using base R & ggplot2 library

  • Statistics using R

    Central Tendency like mean, median, mode, quantile. Variance, Standard deviation, Range, Covariance, Correlation Random samples, Central Limit Theorem

  • Probability Distribution Functions and its Visualization using visualize library

  • Confidence interval, Hypothesis testing, Z-test, T-Test

  • Chi-squared test, ANOVA

  • Univariate and Multivariate regressions

  • Time Series Analysis, ARIMA model

The R programming language serves as a valuable tool for data scientists, analysts, and statisticians, particularly those in academic environments. Its capability to manage intricate analyses like machine learning and financial modeling makes it indispensable for various data-related tasks. This introductory R course delves into the essentials of this open-source language, covering vectors, factors, lists, and data frames. You'll acquire practical coding skills, preparing you to initiate your data analysis in R.
The comprehensive training program will be practical, covering concepts, implementation, and interpretation of the output. It will include multiple exercises on various topics and feature 5 case studies demonstrating various R techniques.
Most asked questions when choosing R Program
I am beginner? can I enroll?

R stands out as an accessible programming language, especially for beginners. Its learning curve is gentler compared to many other languages. This is due in part to the wealth of resources available for learning R, which cater to different learning styles and preferences. Additionally, R boasts a relatively straightforward syntax, making it easier for newcomers to grasp key concepts and start applying them in practice. These factors combined enable beginners to make steady progress in their journey to master R programming.

How R will contribute in my career?

R programming is a versatile language widely used for storing, cleaning, and analyzing data, as well as creating statistical models. Its flexibility is evident in the numerous packages available, which cater to various needs in roles such as data analyst, data architect, analyst manager, market researcher, and business analyst. This makes R programming a valuable skill to learn for anyone interested in working with data across different fields and industries.

Will I get certificate after program completion?

Certainly! Upon successful completion of the Training Program, you will receive a certificate.

Why suddenly R is in so demand?

Firstly, R remains one of the most powerful tools for data analysis and statistical modeling, widely used in academia, research, and industries like finance, healthcare, and technology. Its rich ecosystem of packages, libraries, and tools makes it highly versatile for various data-related tasks.

Secondly, the demand for professionals with R skills continues to grow. Many organizations are looking for data scientists, analysts, and statisticians proficient in R to derive insights from data and drive data-informed decision-making. Learning R can significantly enhance your career prospects in these fields.

Moreover, R’s open-source nature fosters a vibrant community of users and developers, ensuring continuous improvement and innovation. This means that by learning R, you can stay updated with the latest trends and best practices in data analysis and statistical modeling.

Familiarity with the basic computer functions and a good internet connectivity
Training Options
Blended Virtual Classroom
Live Instructor-led
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We have a team of dedicated admissions counselors who are available to guide you as you apply to the program.
They are available and will resolve your below queries
  • We will address to your career related questions
  • We will recommend the program as per your candidature
  • In-detail briefing of the program
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