Rs 24,000
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Important information

Typology Course
Methodology Online
  • Course
  • Online

We create 2.5 quintillion bytes of data every day. So much that 90% of the data in the world today has been created in the last two years.

Business Analytics has thus created opportunities like never before. EduPristine and Dun & Bradstreet's Business analytics certification program will give you the edge in the competitive market.

Where and when
Starts Location


What you'll learn on the course

Business IT

Course programme

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

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