BUSINESS ANALYTICS COURSE

Day 1 & 2 : Basic StatsDay 3: Introduction and Data Analytics and Data Mining

• Introduction to Analytics - OverviewAnalytics v/s Analysis
• Data - Topic covered• Summarizing Data
• • Outlier Treatment
• Case: Categorization of data variablesExploring credit card customer database to define variable types & categorizing them.

Day 4 : Introduction to SAS Language and R Studio

• Introduction to SAS -OverviewMaking familiar with SAS Language
• Introduction to R Studio -OverviewMaking familiar with R Studio Language

Day 5 & 6: Linear Regression and Case Study practice in SAS Language

• Linear Regression – Topic CoveredCorrelation and Regression
• Multivariate Linear Regression Theory
• Bivariate Analysis
• ANOVA (Analysis of Variance)
• Case: Multivariate Linear RegressionIdentify and Quantify the factors responsible for loss amount for an Auto Insurance Company
• Domain CoveredInsurance Industry
• Tool for PracticeSAS Language

Day 7 & 8: Logistic Regression and Case Study practice in SAS Language

• Logistic Regression – Topics CoveredIdentifying problems in fitting linear regression on data having "Binary Response" variable
• Generalized Linear Modeling (GLMs)
• Logistic Regression Theory/Case
• • Fitting the regression using SAS language
• • Lift/Gains chart and Gini coefficient
• • K-S stat
• Case: Multivariate Logistic RegressionIdentify bank customers who will most likely default in making the payment on balance due.
• Domain CoveredBanking Industry
• Tool for PracticeSAS Language

Day 9: Linear Regression + Logistic Regression Case Study Practice in R StudioDay 10 : Upsell Case Study (Logistic Reg ) in SAS Language

• Logistic Regression – Topics CoveredIdentify and develop Dependent variable
• Prepare correlation matrix and VIF chart
• Variable reduction through Multicollinearity
• Perform Binning to prepare modeling dataset
• Run the model
• Write the Scoring or implementation strategy
• Case: Up-Sell ModelPropensity Model for Up-Sell in Telecom Industry
• Domain CoveredTelecom Industry
• Tool for PracticeSAS Language

Day 11: Upsell Case Study + Sentimental Analysis Case Study practice in R

• Sentimental AnalysisProcess of detecting the contextual polarity of text to find whether a piece of writing is positive, negative or neutral.
• Domain CoveredSocial
• Tool for PracticeR Studio

Day 12 : Decision Tree - Chaid & CART

• Decision Tree – Topic CoveredData Mining and Decision Trees
• CHAID analysis
• CART
• Case: CHAID & CART AnalysisIdentifying the classes of customer having higher default rate
• Tool for PracticeR Studio

Day 13: Clustering + Market Basket Analysis in SAS Language

• Clustering - Topic CoveredWhy and Where to use Clustering
• Clustering methods
• K-means Clustering Algorithm
• Case: K-means ClusteringIdentifying similar groups in database containing auto insurance policy records using K-means Clustering
• Domain CoveredInsurance
• Tool for PracticeSAS Language
• Association Rule – Topic CoveredAffinity analysis to understand purchase behavior
• Understanding Apriori algorithm
• Analysis of output results to plan store layout, promotions and recommendations
• Case : Market Basket AnalysisUnderstanding apriori algorithm to identify affinity among the purchase data in the basket based on historical transactions.
• Domain CoveredRetail Industry
• Tool for PracticeSAS Language

Day 14: Clustering + Market Basket Analysis Case Study practice in R StudioDay 15 & 16: Time Series Modeling and ARIMA Modeling

• Logistic Regression – Topics CoveredModels of time series
• The Box-Jenkins model building process
• Identify the ARIMA model.
• Forecasting future sales based on historical data for an automobile company.
• Case 1: Time Series Modeling using R
• Case 2: ARIMA ModelingIdentify bank customers who will most likely default in making the payment on balance due.
• Domain CoveredAutomobile Industry
• Tool for PracticeR Studio