R Programming

Course Features

Course Details

Mastering R Programming

R Programming the most trending and highest paid Programming Jobs. Enrol Today!

Learn R Programming from myTectra the market leader !

Join myTectra to Up-Skill on the most popular programming languages R Programming !

Training Features

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes. Weekend Class : 10 sessions of 3 hours each. Weekday Class : 15 sessions of 2 hours each.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Real-life Case Studies

Live project based on any of the selected use cases, involving real time project of the various R Programming concepts.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.


Live project based on any of the selected use cases, involving of the various R Programming concepts.


Towards the end of the course, you will be given access to online Test. myTectra certifies you as an R Programming Expert based on the scoring of 60% or above.

Course Outline

Chapter 1:Introduction to Data Analytics

  • Understand Business Analytics and R
  • Knowledge on the R language
  • community and ecosystem
  • Understand the use of the industry
  • Compare R with other software in analytics
  • Install R and the packages useful for the course
  • Perform basic operations in R using command pne
  • Learn the use of IDE R Studio and Various GUI
  • Use the feature in R
  • Knowledge about the worldwide R community collaboration.

CHAPTER 2:Introduction to R Programming

  • The various kinds of data types in R and its appropriate uses
  • The built-in functions in R pke: seq(), cbind (), rbind(), merge(), Knowledge on the various Subsetting methods, Summarize data by using functions pke: str(), class(), length(), nrow(), ncol(), Use of functions pke head(), tail(), for inspecting data, Indulge in a class activity to summarize data.

CHAPTER 3:Data Manipulation in R

  • he various steps involved in Data Cleaning
  • Functions used in Data Inspection
  • Tackpng the problems faced during Data Cleaning
  • Uses of the functions pke grepl(), grep(), sub(), Coerce the data, Uses of the apply() functions.

CHAPTER 4:Data Import Techniques in R

  • Import data from spreadsheets and text files into R
  • Import data from other statistical formats pke sas7bdat and spss
  • Packages installation used for database import
  • Connect to RDBMS from R using ODBC and basic SQL queries in R
  • Basics of Web Scraping

CHAPTER 5:Exploratory Data Analysis

  • The Exploratory Data Analysis(EDA)
  • Implementation of EDA on various datasets
  • Boxplots
  • Understanding the cor() in R
  • EDA functions pke summarize(), lpst(), Multiple packages in R for data analysis
  • The Fancy plots pke Segment plot, HC plot in R.

CHAPTER 6:Data Visuapzation in R

  • Understanding on Data Visuapzation
  • Graphical functions present in R
  • Plot various graphs pke tableplot
  • Histogram
  • Boxplot
  • Customizing Graphical Parameters to improvise the plots
  • Understanding GUIs pke Deducer and R Commander
  • Introduction to Spatial Analysis.

CHAPTER 7:Data Mining: Clustering Techniques

  • Introduction to Data Mining
  • Understanding Machine Learning
  • Supervised and Unsupervised Machine Learning Algorithms
  • K-means Clustering.

CHAPTER 8:Data Mining: Association Rule Mining and Sentiment Analysis

  • Association Rule Mining
  • Sentiment Analysis.

CHAPTER 9:Pnear and Logistic Regression

  • Pnear Regression
  • Logistic Regression.

CHAPTER 10:Anova and Predictive Analysis

  • Anova
  • Predictive Analysis.

CHAPTER 11:Data Mining: Decision Trees and Random Forest

  • Decision Trees
  • Algorithm for creating Decision Trees
  • Greedy Approach: Entropy and Information Gain Creating a Perfect Decision Tree, Classification Rules for Decision Trees, Concepts of Random Forest, Working of Random Forest, Features of Random Forest.

CHAPTER 12:Project

  • Analyze Census Data to predict insights on the income of the people, based on the factors pke : Age, education, work-class, occupation, etc.

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