R programming for Data science

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Why this course ?

  • R is most popular and  the leading open source language in data science and statistics
  • Today, R language is the choice for most data science professionals in every industry and academics.
  • This course is thoroughly described R programming, Statistics and Data Science for beginners using real life examples.
  • This Course covers both the theoretical aspects of Statistical concepts and the practical implementation using R.

R is most popular and  the leading open source language in data science and statistics. Today, R language is the choice for most data science professionals in every industry and academics.

Program Duration
and Fees

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Duration

8 Hours

Price

12800

Features

  • Why Hands-On Online Training ?
    A chance to learn Wherever and Whenever.
    Why Digital Learning ?
    Digital Learning can improve memory performance.....
    Budget
    Cost-effective training program.

Description

• What is R
• What is S
• History of R
• Features of R
• SAS versus R
• Installing R
• Packages
• Input/output
• R interfaces
• R Library
Vector
• Lists & Matrix
• Array
• Data Frame
• 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
• Merging and Sorting Functions in R
• Summarising Data
• Concept of hypothesis
• Null hypothesis
• Alternative hypothesis
• Type-I error
• Type-II error
• Level of Significance
• Confidence Interval
• 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
• 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
• Semi-Partial R-Square and R-Square
• Diagrammatic Representation of clusters
• Problems of Cluster Analysis
• Types of clustering
• Hierarchical Clustering
• Principal Component Analysis
• 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
• Concept of Time Series and its Applications
• Assumptions of Time Series Analysis
• Components of Time Series
• Smoothening techniques
• Stationarity
• ARIMA Forecasting
• Box Jenkins Technology
TEXT MINING ANALYSIS
DECISSION TREES
MARKET BASKET ANALYSIS
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Course Name: R programming for Data science

12800