R programming


Why this course ?

  • The R programming language leans more frequently to the cutting edge of data science, giving businesses the latest data analysis tools.
  • Students or professionals who want to make a career progression in data science should learn R through a comprehensive training .
  • Demand for data scientists is greatly increasing. Demand for R developers will no doubt be on the rise.
  • R language is the magic wand for data scientists to handle and analyse huge amounts of complex and unstructured data productively.

Data Science with R

Program Duration
and Fees


54 Hours




  • Advantages of attending the training
    Join the classroom/online training.
    Led by an expert instructor
    Led by an expert instructor who can virtually look over your shoulder
    Real-time answers
    Ask questions and get answers in real-time.Discuss, share, exchange ideas.


• Types of Analytics
• Properties of Measurements
• Scales of Measurement
• Types of Data
• Measures of Central Tendency
• Measures of Dispersion
• Measures of Location
• Presentation of Data
• Skewness and Kurtosis
• Three Approaches towards Probability
• Concept of a Random Variable
• Probability Mass Function
• Probability Density Function
• Expectation of A Random Variable
• Probability Distributions
• Concept of population and sample
• Techniques of Sampling
• Sampling Distributions
• Concept of estimation
• Different types of Estimation
• Concept of hypothesis
• Null hypothesis
• Alternative hypothesis
• Type-I error
• Type-II error
• Level of Significance
• Confidence Interval
• Parametric Tests and Non Parametric Tests
• One Sample T test
• Two independent sample T test
• Paired Sample T test
• Chi square Test for Independence of Attributes.
• One Way Anova
• Two Way Anova
• Principal Component Analysis
• Estimating the Initial 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
• Types of Clusters
• Metric and linkage
• Ward’s Minimum Variance Criteria
• Semi-Partial R-Square and R-Square
• Diagrammatic Representation of clusters
• Problems of Cluster Analysis
• Concept of Regression and features of Linear line.
• Assumptions of Classical Linear Model
• Method of Least Squares
• Understanding the Goodness of Fit
• Test of Significance of The Estimated Parameters
• Multiple linear Regression with their Assumptions
• Concept of Multocollinearity
• Signs of Multicollinearity
• The Idea Of Autocorrelation
• Concept and Applications of Logistic Regression
• Principles Behind Logistic Regression
• Comparison between Linear probability Model and Logistic Regression
• Mathematical Concepts related to Logistic Regression
• Concordant Pairs, Discordant Pairs and Tied Pairs
• Classification Table
• Graphical Representation Related to logistic Regression.
• 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 Technology
• Support
• Confidence
• List
• Processing Huge text file
• Stopwords
• Sentiment score




Course Name: R programming