PART 1:  Basic Stats

Chapter 1: Basic Stats

  • Basic Stats

Chapter 2: Introduction and Data Analytics

  • Overview
  • Analytics v/s Analysis
  • Outlier Treatment

Chapter 3: Linear Regression

  • Correlation and Regression
  • Multivariate Linear Regression Theory
  • Bivariate Analysis
  • ANOVA (Analysis of Variance)
  • Multivariate Linear Regression
  • Identify and Quantify the factors responsible for loss amount for an Auto Insurance Company
  • Insurance Industry

Chapter 4: Logistic Regression

  • Identifying problems in fitting linear regression on data having "Binary Response" variable
  • Generalized Linear Modeling (GLMs)
  • Logistic Regression Theory/Case
  • Fitting the regression
  • Lift/Gains chart and Gini coefficient
  • K-S stat

Chapter 5: Multivariate Logistic Regression

  • Identify bank customers who will most likely default in making the payment on balance due
  • Domain Covered Banking Industry

Chapter 6: Decision Tree and Clustering

  • Data Mining and Decision Trees
  • CHAID analysis
  • CART
  • Why and Where to use Clustering
  • Clustering methods
  • K-means Clustering Algorithm

Chapter 7: CHAID & CART Analysis

  • Identifying the classes of customer having higher default rate
  • K-means Clustering
  • Identifying similar groups in database containing auto insurance policy records using K-means Clustering
  • Domain Covered-Insurance and Banking Industry

Chapter 8:Market Basket Analysis

  • Affinity analysis to understand purchase behavior
  • Understanding Apriori algorithm
  • Analysis of output results to plan store layout, promotions and recommendations
  • Understanding apriori algorithm to identify affinity among the purchase data in the basket based on historical transactions.
  • Retail Industry
  • Using Dictionary Tables