R – Introduction: What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work
Directory.

R Implementation – Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions
R Data Structures – Vectors and Lists – Strings and Matrices – Arrays and Factors – Data
Frames – Packages.
R Interfaces – R- CSV files Read and Write and analyze the data – R- Excel files Read and
Write and analyze the data

Data Visualization Using R Software- Introduction to Visualisation – Line Plots and Bar
Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting
– Exporting Plots and Other Plotting Packages

Predictive Customer Analytics using R – Linear Regression using R Software – Linear
Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics
Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis –
Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with
Real Time Example.

Bank Loan Modelling using R – Logistic Function – Single Predictor Model – Determine
Logistic Cut off – Estimated Equation for Logistic Regression

Sales Promotion Effectiveness -Dimension Reduction using R Software – Factor Analysis –
Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor
Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis
Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with
Real Time Example

Customer and Market Segmentation – Cluster Analysis using R Software – Cluster Analysis
Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis –
Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering .

Retail Analytics: Market Basket Analysis (MBA) – Association Rule using R Software –
Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market
Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis

Customer Loyalty Analytics- Naïve Bayes Classification using R Software – Naïve Bayes
Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve
Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve
Bayes

K – Nearest Neighbour (KNN) Using R Software – K – Nearest Neighbour Introduction – K –
Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure
“Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN –
Advantage and Disadvantage of KNN

Decision Trees using R Software – What is a Decision Tree? – How to create Decision Tree –
Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with
Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree
Replacement and Raising – Real time case study with Decision Tree

Random Forest using R Software – Ensample of Decision Tree.

Support Vector Machine – SVM – Linear SVM using Hyperplane – Non-Linear Hyperplane
using Kernal Trick and Advantage and Disadvantage of SVM

Real Time Project – Customer Loyalty Analytics and its Application using R Software- RFM
Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling
using Operational Analytics – Fundamentals and Modelling Framework – Industry application
– Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework
– Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity
and Optimization – Promotion Effectives using Analytics

Real Time Project – Finance Analytics and its Application using R Software- Credit Risk
Analytics using Logistic Regression – Merger and Acquisitions Analysis using Financial
Analytics – Company’s Short Term Performance Analysis using Analytics – Company’s Long
Term Performance Analysis Using Analytics – Financial Ratio Analysis using Analytics –
Profitability Ratio Analysis using Financial Analytics – Leverage Ratio and Liquidity Ratio
Analysis using Financial Analytics – Efficiency and Valuation Ratio Analysis using Financial
Analytics – Company’s Financial Analytics – Sector wise Analysis Using Financial Analytics –
Industry wise Analysis Using Financial Analytics – Individual Company Analysis Using
Financial Analytics – Loss Mitigation and Revenue Growth Analysis