Rs 45,000
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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.

Important information

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

Achievements for this centre

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