R Programming for Data Science - Advance

Overview

This course will make participants enable to create a Shiny application through data to data manipulation and charting with the touch of statistical fundamentals.

Availability: In stock

Regular Price: ₹25,999.00

Special Price ₹24,999.00

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Regular Price: ₹25,999.00

Special Price ₹24,999.00

Upcoming Classes

12th Jan 2019
25 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 24999
16th Mar 2019
25 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 24999

Course Description

  • Course will start with set of objective questions which will give instructor idea of participants and also it will encourage participants to learn 
  • Every fundamental of learning is backed by objective questions and hands on 
  • Course with last with set of multiple choice questions which demonstrate the improvements in participants.
  • Course will last for 12 days covering 72 hours

Course Instructor : Raju Kumar Mishraview more

Objective

R Package participants will learn

  • base r
  • plyr
  • dplyr
  • stringr
  • ggplot2
  • xml2
  • foreign
  • xlsx
  • RmySQL
  • shiny
  • jsonlite

Detail Description of course:

Basic Introduction to R 

  • Introduction to R
  • Drawback of using R

 Getting help 

  • help ()
  • Mailing List
  • R Web Page
  • ? Operator
  • ?? Operator
  • Hands on Exercise 

Structure of program in R 

  • Using R console
  • Scripting in R 

Packages: 

  • Type of packages
  • Introduction to R Base Packages
  • Introduction User Created Package
  • Brief introduction to some user created packages
  • Package Installation
  • Hands on Exercise 

Basic Data type 

  • Integer
  • Numeric
  • Character
  • Logical
  • Complex
  • Special data type 

Advance data objects 

  • Vector
  • List
  • Matrices
  • Array
  • Table
  • Data Frame
  • Naming row and column of data frame and matrix
  • Hand on Exercise 

Simple Statistic In R 

  • Mean
  • Median
  • Mode
  • Covariance
  • Correlation
    • Pearson
    • Spearman
    • Interpreting Correlation 

 

Loops and conditional 

  • Use of loop and conditionals
  • Structure of conditionals
  • if statement
  • if, else statement
  • if ,else if , else statement
  • while loop
  • for loop
  • Repeat
  • Hand on Exercise 

IO in R 

  • General file structure.
  • csv files
  • excel files
  • JSON
  • XML

Advanced loop 

  • apply ()
  • sapply ()
  • laaply ()
  • tapply ()
  • by ()
  • Hands on exercise 

Data Manipulation with plyr and dplyr 

  • Introduction to plyr and its components.
  • xxply function of plyr
  • Introduction to dplyr
  • Data manipulation with dplyr 

Date and Time in R 

  • Introduction to date and times.
  • Problem with date and time.
  • Introduction to lubridate.
  • Date and time manipulation 

String Manipulation in R 

  • Basic of String
  • Understanding String operations.
  • Important String Operations
    • String split
    • String Substitution.
    • Sub Strings finding.
    • Finding pattern
  • Regular Expression in R
  • Introduction to StringR packages
  • Stringr functions in detail
  • Hands on Exercise 

Function in R 

  • Introduction to function in R
  • Structure of function
  • Returning a value from a function
  • Returning complex data type from a function
  • Recursion
  • Hands on exercise 

Some mathematical functions 

  • Finding minimum maximum
  • Trigonometric function
  • Exponential function
  • Logarithm calculation
  • Finding absolute value
  • Factorial function
  • Cumulative mathematical functions
  • Pmin ()
  • Pmax ()
  • Round ()
  • Floor ()
  • ceiling ()
  • sqrt () 

Set Operations in R 

  • Defining set
  • Set properties
  • Union
  • Intersection
  • Subtraction 

Graphics in R: 

  • Use of graphs and chart
  • Basic elements of graph
  • Graphics in R base package
  • par()
  • plot()
  • Basic elements of graph generation
  • ggplot2 package
  • Grammar of graphics
  • Layered structure of ggplot2
  • Basic elements of ggplot2
  • qplot()
  • ggplot()
  • Some chart use and creation with Base R and ggplot2 package
    • Bar chart
    • Stacked Bar Chart
    • Histogram
    • Scatter plot
    • bubble chart
    • Pie chart
    • quantile quantile plot
    • Box Plot
    • Area Plot
    • Multiple plots
    • Line graph (Time Series Plotting)
    • Writing plot to files
  • Hands on Exercise 

R connection with Database 

  • Introduction to RDBMS
  • Introduction to MySql
  • R packages to connect to database
  • Data analysis of data from database
  • Hands on Exercise 

Debugging in R 

  • Introduction to Debugging
  • Some useful function to debug
  • browser()
  • debug()
  • undebug()
  • debugonce()
  • trace()
  • untrace()
  • setBreakPoint()
  • Hands On Exercise 

Shiny introduction 

  • Introduction to Shiny.
  • Concept of client and Server
  • Shiny application
  • Shiny application main components.
  • Creating first Shiny application. 

Shiny widgets 

  • Introduction to Widgets.
  • Widgets in Shiny
  • Control Widgets.
  • Different control widgets and their applications.
  • Understanding Page Layouts 

Data and R Script integration in Shiny 

  • Data integration
  • R Script integration 

Reactivity 

  • Introduction to Reactive expression
  • Reactive expression behavior
  • Creating reactive variables
  • Accessing reactive variables 

HTML and Shiny 

  • HTML tags in Shiny
  • HTML templates in Shiny 

Linear Regression:

  • Introduction to simple linear regression.
  • Business use cases of Linear regression.
  • Assumptions of simple linear regression.
  • Parameter calculation.
  • Function lm()
  • Multiple linear regression.
  • F-test on coefficient selections.
  • Step up and step down methods.
  • Other methods of independent variables selection.
  • Package leaps in R.
  • Validation of linear regression assumptions
  • Problem of multicollinearity.
  • Qualitative independent variables.
  • Lasso and Ridge regression.
  • Inference from results.

Classification:

  • Introduction to classification.
  • Business use cases of classification.
  • Approach of classification. 

Logistic regression:

  • Introduction to logistic regression.
  • Mathematical development of logistic model.
  • Result interpretation
  • Classification evaluation metrics introduction.
  • Result evaluation.
  •  R function glm() 

Classification Evaluation metrics :

  • Confusion metrics.
  • Sensitivity.
  • Specificity.
  • ROC curve.
  • Area under curve.
  • Package caret

Decision tree :

  • Introduction to decision tree.
  • Classification and regression tree.
  • Splitting algorithms
    • ID3
    • C4.5
    • CART
  • Tree pruning
  • R package rpart
  • R package tree
  • Inference of results

Ensemble learning :

  • Introduction to ensemble learning.
  • Random forest
  • R library randomForest

Bayes Classification :

  • Introduction to Bayes theorem.
  • Naive Bayes classification.
  • R package e1071 

Neural Networks :

  • Introduction to Neural network.
  • Basic idea about brain.
  • Perceptrons
  • Activation Functions
  • Multilayer Perceptrons
  • Feed Forward networks
  • Error back propagation algorithm
  • R package nnet

Clustering :

  • Introduction of clustering.
  • Business use cases of Clustering.
  • Clustering approach
    • Partitioning algorithms
    • Hierarchy algorithms
    • Density based
  • Introduction to R package “cluster” and other clustering methods in R base package.
  • K means clustering
  • K medoides (PAM)
  • Hierarchical clustering
  • BIRCH
  • DBSCAN
  • Comparison of different clustering algorithms and model evaluations
  • R package cluster

Market Basket Analysis :

  • Introduction to market basket analysis.
  • Business use cases for market basket analysis
  • Apriori Algorithm
  • FP Growth algorithms
  • R package arules 

Text Analysis in R:

  • Introduction to text analysis.
  • Introduction to R library “tm”.
  • Business use cases of Text analysis.
  • Approaches to do text analysis.
  • Word Clouds.
  • R package word clouds

Recommendation system:

  • Introduction to recommender system.
  • SVD and other matrix factorizations.
  • Classification and Recommendation.
  • Matrix factorization and Recommendation 

Introduction to deep learning

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