R Programming

Currency:

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.

Classroom OR Online

Program Duration
and Fees

Duration

54 Hours

Price

N/A

Features

  • Advantages of attending the training
    Join the 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.

C. R PROGRAMMING

Description

• 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.

D. R ANALYTICS

Description

• What is R
• What is S
• History of R
• Features of R
• SAS versus R

Description

• Concept of Decision tree
• Hands-On session on Decision tree
• 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.
• Installing R
• Packages
• Input/output
• R interfaces
• R Library
• 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
• 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
• 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
• Processing Huge text file
• Stopwords
• Sentiment score
• Support
• Confidence
• List
• 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
• One Way Anova
• Two Way Anova
• Basic Operations in R
• Different Data Types And Data Structures In R
• Subsetting In R
• Vectors
• Logical Operator
• if ELSE(Conditional Processing)
• loops
• While loop
• Functions
• Create own function
• create matrices
• colnames() rownames()
• Matrix Operations
• Subset matrices
• Import Data
• Operations on data frame
• Application on data frame
• Filter your data
• Array and data manipulation
• Plots And Charts In R
• Merging and Sorting Functions in R
• Summarizing Data
• Vector
• Lists & Matrix
• Array
• Data Frame

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Course Name: R Programming