Module 1: Introduction

• What is Analytics
• Types of Analytics
• Financial meets Analytics
• Organisations using Financial  Analytics                                                                                                               

Data Collection & Data Understanding – Basics

• Data Dictionary
• Data types
• Data Handling

Outlier Treatment

Handling Missing Values

Financial Data using - EXCEL

• MS Excel Functions

• Pivot Tables

• MS Excel Charts

• Case Study

R Programming

• R-studio

• Understanding Basics

• Packages in R

• Swirl R

• Summarise & slice data

• Case Study

 Linear Regression

• What is regression?

• Introduction to Linear Regression

• Applications & examples from the financial world

• Case Study

• Understanding Data using R

  • Uni-variate analysis

  • Bi-variate analysis

• Best fit line – regression

• R-square, Adjusted R-square concept

• Test vs. Train datasets ( concept )

• Running the regression in R

• Linear Equation & significant variables

• Assumptions of Linear Regression

• Testing Multi-collinearity

• Heteroskedasticity

• Case Study

Module 2: Logistic Regression

• Classification techniques & financial problems

• Intro to Logistic Regression models

  • Why Logistic vs. Linear models

  • Odds Ratio

  • Probability of an event

• Applications of Logistic regression models

• Case Study

• Generalized Linear models using R

• Shortlist the significant variables

• Test vs. Train data sets

• Validation of the model – Confusion matrix

• Evaluate the model and give business recommendations.

Module 3: Decision Tree 

• Introduction

• Applications & industry examples

• Types of Decision Tree ( CART & CHAID )

• Splitting Algorithms

• Case Study

• R-code

• Interpretation

• Final o/p

• Case Study: Net Promoter Score (understand NPS & its Promoters by uncovering the relationship between the variables to devise customer satisfaction improvement strategy)

• R-code

• Interpretation

• Final o/p

Module 4:  Clustering

K-Means

• Financial analytics & unsupervised techniques

• Why and Where to use Clustering

• Clustering methods and examples

• K-means Clustering Algorithm

  • K-means – Cluster the given set of customer base to help in segmentation to help identify different marketing campaigns for each cluster

  • Practice the k-means using R codes

RFM- Recency Frequency Monetary models

• Identify differences in behaviour of

  • Online shoppers

  • In-store shoppers

  • Multi-channel shoppers

Market Basket Analytics 

• Intro to the Basics

• Applications

• Algorithm

• Case Study

  • R-code

  • Interpretation

  • Final o/p

Framework to solve analytics case studies

• ​​CRISP – DM & other known frameworks