R Programming for Data Science - Advance

• Course will start with set of objective questions which will give instructor an 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

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