R Programming

R Programming

  • 8 weeks
  • 48 hours

Learn R from the scratch. Join this comprehensive course to kick-start your career in this sought-after field. Join this course to make yourself 100% job ready..

Course Overview

This course is designed to master techniques like data exploration, data visualization, and predictive analytics and descriptive analytics with the R programming language. The course covers the import and export of data in R, data structures in R, different statistical concepts, cluster analysis, and forecasting. Student will gain an understanding of analyzing data to help companies make more effective business decisions.


This Professional Certificate from Handson has a comprehensive course curriculum covering Statistics, data structures and more – with a great detail via our interactive learning model so that you are comfortable with any kinds of questions asked later on. Upon successfully completing this course, you will be able to fast track your career in the field of interest and it will help you to kick start into an exciting profession.

What You'll Learn
  • Introduction to Data Science

  • Programming I :Essential

  • Programming II:Data Step Manipulation

  • History of R-language
  • Why to learn R-language
  • Importance of R-language
  • . Installation and setup Environment
  • Packages interfaces and library
  • Expressions and Operations
  • Data Types and Data Structures- Vectors, Factors,Matrix, Dataframes,Lists
  • Vector Basics
  • Vector Operations
  • Vector Indexing and Slicing
  • Matrix Operations
  • Data Frame Indexing and Selection
  • Operations on Data Frame
  • CSV Files with R
  • Operators
  • Conditional Statements
  • Loops & Functions
  • Built-in R Features & Apply
  • Dates and Timestamps
  • Understanding & Working with Graph Libraries
  • Overview of ggplot2
  • Histograms
  • Scatterplots
  • Bar Plot
  • Boxplots
  • 2 Variable Plotting
  • Sorting, Concatenation of Datasets
  • Concept of Hypothesis
  • Null Hypothesis
  • Alternative Hypothesis
  • Type-I error
  • Type-II error
  • Level of Significance
  • Confidence Intervals
  • Parametric Tests and Non Parametric Tests
  • One Sample T test
  • Paired Sample T test
  • Chi-square Test for Independence of Attributes
  • Principal Component Analysis
  • Concept of Communalities
  • Eigen Values and Eigen Vectors
  • Correlation Matrix check and KMO-MSA check
  • Factor loading Matrix
  • Diagrammatic Representation of Factors
  • Problems of Factor Loadings and Solutions
  • Introduction to Machine Learning
  • Data Munging in R
  • Cyclical vs Seasonal Analysis
  • One Way Anova
  • Two Way Anova
  • Concept of Linear Regression
  • Important features of a Straight line
  • Method of least Square
  • Assumptions of Classical Linear Regression Model
  • Understandig the Goodness of Fit
  • Test of Significance of the Estimated parameters
  • Concept of multicollinearity
  • Concept of VIF
  • Concept of AutoCorrelation
  • Practical Application of Linear Regression using R
  • Concept of Logistic Regression
  • Differences between Linear Regression and Logistic Regression
  • Logistic Regression Model
  • ODDS AND ODDS RATIO-Mathematical Concepts
  • Concept of Concordant Pairs, Discordant Pairs, Tied Pairs
  • Confusion Matrix and its Measures
  • Determining the Cut-Point Probability Level
  • Receiver Operating Characteristic Curves
  • Practical Application of Logistic Regression using R.
  • Concept of Time Series and its Applications
  • Assumptions of Time Series Analysis
  • Components of Time series
  • Smoothening techniques
  • Stationarity
  • Random Walk
  • ARIMA Forecasting
  • Box Jenkins Technology
  • Merits and Demerits of BJ TechnologyMerits and Demerits of BJ Technology
  • Concept of Decision Tree
  • Decision Tree Application using R
  • Concept of K-Means Clustering
  • Types of Cluster Analysis
  • Concept of Linkage
  • Ward’s Minimum Variance Criteria
  • Clustering related Statistics-Semi-Partial R-Square,R Square
  • Graphical Representation of Cluster Analysis
  • Practical Application of Clustering using R
  • Concept of Text Mining and Sentiment Analysis
  • Concept of Stopwords
  • Practical Application of Text Mining and Sentiment Analysis
  • Concept of Market Basket Analysis
  • Measures of Market Basket Analysis-Support,lift,Confidence
  • Advantages of Market Basket Analysis
  • Practical Application of Market Basket Analysis
Admission Process

Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor



Who Can Join?

Any graduate with knowledge of basic computing.


1. Personal computer/laptop with webcam and microphone

2. Stable internet connections

Payment Details

Bank Details:
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis

This Course Include:

  • Live Instructor-Led Course
  • Project and Case Studies
  • Certificate of completion
  • Learn from Experts
  • Placement Assistance
  • Assistance for Global Certification