Why R Training?

It is always recommended that you start learning one language along with learning data science. R is always the best first data science programming language. Firstly, it is widely used and is very popular with over 2 million users worldwide. It is estimated that every year the number of R users grow by over 40 per cent. R is used in many companies that hire data scientists. It is used extensively in tech companies like Facebook and Microsoft. Other companies that use R for data science are Bank of America, Uber, TechCrunch, Ford, etc. R as a programming language is also popular among academicians and scientists.

Learn R programming language possesses significant capabilities in areas such as data visualization, data manipulation and machine learning. R is an easy language to learn. These are reasons enough to learn R programming language.


The eligibility criteria for someone that wants to learn R programming are as follows:

  • Students who need to learn ‘R Programming’ as part of their course
  • Web developers who want to incorporate data analysis features in their web pages
  • Anyone that is interested in data science and statistics Data analysis researchers
  • Data Analytics professionals or those working in related fields
  • Detailed Course Curriculum

    R is a suite of software programs that allow data manipulation, calculations and graphical displays.

  • It contains data handling and storage facilities
  • It contains operators to perform calculations on matrices/arrays
  • It contains tools for data analysis
  • Graphical tools for data analysis with on-screen display or as print copies
  • A simple programming language that includes loops, condition statements, recursive functions that can be defined by the user and input and output facilities.
  • Introduction to R

    Section 1: Introduction to R Basics

  • How it works
  • R Arithmetic
  • Assignment of variables
  • Assignment of variables – 2
  • Assignment of variables – 3
  • Basic data types in R
  • Section 2: Vectors
  • Creating a vector
  • Creating a vector – 2
  • Naming a Vector
  • Naming a vector -2
  • Calculating totals
  • Comparing Totals
  • Vector Selection – 1
  • Vector Selection – 2
  • Vector Selection – 3
  • Vector Selection – 4
  • Selection By comparison
  • Advanced Selection
  • Section 3: Matrices

  • What is a matrix
  • Analysis of matrices
  • Naming Matrices
  • Adding columns
  • Adding rows
  • Selection of elements
  • Arithmetic with matrices – 1
  • Arithmetic with matrices – 2
  • Section 4: Factors

  • What is a factor
  • Why you should use factors
  • Factor Levels
  • Summarizing factors
  • Ordered Factors
  • Comparing factors
  • Section 5: Data Frames

  • What is a data frame
  • Data Sets
  • Structures
  • Creating a Data Frame
  • Selection of elements in a data frame Sorting
  • Sorting a data frame
  • Section 6: Lists

  • Why you need lists
  • Creating a list
  • Creating a named list
  • Selecting elements from lists
  • Adding more information to a list
  • Intermediate R

    Section 1: Conditions and Flow of Control

  • Relational operators
  • Equality, Greater than, and Less than operators
  • Compare vectors
  • Compare Matrices
  • Logical operators
  • & and I
  • Reverse the result – ! operator
  • Conditional statements
  • If statement
  • If-else Statement
  • Section 2: Loops

  • While loop
  • How to write a while loop
  • More conditions
  • Break from the while loop
  • Build a whole new while loop
  • For Loop
  • Loop over a vector
  • Loop over a list
  • Loop over a matrix
  • Mix with flow of control
  • Break the for loop
  • Section 3: Functions

  • Introduction to functions
  • Documentation of functions
  • How to use a function
  • Function Inside a function
  • Writing functions
  • Create your own functions
  • Passing arguments by value
  • R Packages
  • Different methods of loading packages
  • Section 4: Apply Family

  • Iapply
  • Iapply with inbuilt R function
  • Iapply with own function
  • Iapply with anonymous functions
  • Iapply with additional arguments
  • Apply functions which return NULL
  • sapply
  • How to use sapply
  • sapply with own function
  • sapply with function that returns vector
  • sapply with function that returns NULL
  • vapply
  • How to use vapply
  • sapply to vapply
  • Section 5: Utilities

  • Functions that are useful
  • Mathematical utilities
  • Data Utilities
  • Regular expressions
  • Error finding
  • grepl & grep
  • Regular expressions
  • sub & gsub
  • Date and time
  • Right here, right now
  • Create, format dates
  • Create, format times
  • Calculations with dates and times
  • Other major topics:

  • Data Scientist with R
  • Quantitative Analyst with R
  • Data Visualization with R
  • Importing Data and cleaning it with R
  • Job Opportunities

    The following are some of the job openings for those proficient in R programming:

  • Data Scientist
  • Language Data Specialist
  • Quantitative R Developer
  • Natural Language Processing
  • Machine Learning Using R Instructor Specialist
  • Senior Applied Scientist
  • Researcher
  • Employer Expectations

    Once you have completed R training, employers expect you to be good at deriving insights and statistical computation from volumes of data. Remember that age does not play a role here. It is only the skills that have been learnt that matter.

    When companies are hiring R trained candidates, they are expected to have:

  • Strong problem-solving skills
  • Good Verbal and written communication skills
  • Knowledge of statistics/economics
  • Strong attention to detail
  • Proficiency in R Programming
  • Statistical clarity and some amount of interest in predictive techniques (regression, Bayesian methods, etc.)
  • Structured thinking
  • Course Objectives

    The R programming course is designed with the following objectives in mind:

    The course is based on real-world problems so that the student can apply quickly whatever he/she has learned. The course also enables the student to hone the new skills that they have picked up. R Programming Training in Chennai helps to improve knowledge and skills in data visualization, statistics, data manipulation and machine learning.

    R Training Center in Chennai Venue:

    Are you located in any of these areas – Adyar, Ambattur, Aminjikarai, Adambakkam, Anna Nagar, Anna Salai, Ashok Nagar, Besant Nagar, Choolaimedu, Chromepet, Egmore, Ekkattuthangal, Guindy, K.K.Nagar, Kilpauk, Kodambakkam, Madipakkam, Medavakkam, Mylapore, Nandanam, Nanganallur, Nungambakkam, OMR, Pallikaranai, Perungudi, Porur, Saidapet, Sholinganallur, St. Thomas Mount, T. Nagar, Tambaram, Teynampet, Thiruvanmiyur, Thoraipakkam,Vadapalani, Velachery, Villivakkam, Virugambakkam and West Mambalam.

    Our T Nagar or Velachery office is just few kilometre away from your location. If you need the best R Training in Chennai, driving couple of extra kilometres is worth it!

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