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© EduPristine Business Analytics © EduPristine – www.edupristine.com

Business Analytics Course Catalogue

© EduPristine Business Analytics

Business Analytics

1


Objective

Business Analytics is a specialized course designed to deliver knowledge on application of statistical concepts in real world scenarios. This course is designed to equip professionals working in Finance, Marketing, Economist, Statistical, Mathematics, Computer Science, IT, Analytics, Marketing Research, or Commodity markets with the essential tools, techniques and skills to answer important business questions.

Participants will be able to:

Explore data to find new patterns and relationships (data mining)

Predict the relationship between different variables (predictive modeling, predictive analytics)

Predict the probability of default and create customer Scorecards (Logistic Regression)

After completion of this program, the participants

Understand a Problem in Business, Explore and Analyze the problem

Solve business problems using analytics (in R) in different fields

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Pre-requisites

Pre-requisites for the course:

The participants are expected to have the basic understanding of the following topic:

Basic Statistics*

*Edupristine provides comprehensive recordings of basis statistics concept along with its Business Analytics course ware.

Note: Office laptop doesn’t allow to install some of the software’s , so please check and install the required software before coming to class. For any technical issues contact respective city co-ordinator.

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Training Snapshot

Training Highlight

10 Days Classroom Training (50 Hours)

50 Hours Virtual Lab Practice (On SAS Language)

Online Support through Webinar (Twice in a Month)

Pre-requisite Video Tutorial on Basic Statistic and Data

Forum to Discuss with Follow Students and Experts

Lecture Handout

Downloadable Course Material

Tool used for Training – MS Excel ; R package ; and SAS Language

24 * 7 Access to Online Materials

Certificate of Completion / Excellence

4


Day 1: Introduction and Data Analytics

5

Introduction to Analytics - Overview Analytics v/s Analysis Business Analytics Business domains within Analytics

Data - Topic covered

Summarizing Data Data Collection Data Dictionary Outlier Treatment

Case: Categorization of data variables Exploring credit card customer database to define the variable types and categorizing each type into relevant group.

Tool for Practice in Class MS Excel

Introduction to Commonly used Tools in Analytics

R Software SAS Language

Pre Class Requirement Download and Install R from Install MS Excel (2007 or 2010) Check for your online Account details along with SAS Language Lab Hours Online Course Material Be familiar with Case study to be discussed in the class

Post Class Assignment Practice the case study discussed in the class and watch the video tutorials on Basic statistic (provided as pre-requisites material)


Day 2: Multichannel Segmentation

6

Multichannel Segmentation – Topic Covered Identify differences in behavior of online, in-store & multi-channel shoppers Identify the size of the opportunity for growth and begin to identify the methods to achieve it The value of the different shopper groups Key measures to look at: Spend per visit Spend per shopper Units per visit Units per shopper Frequency of Purchase (Visits per shopper)

Case : Retail Analytics Case Synopsis : Understanding the value opportunity of focusing on the different shopper groups and framing analysis to better understand the multi-channel shoppers.

Domain Covered Retail Industry

Tool for Practice in Class R

Tool for Demonstration SAS Language

Pre Class Requirement Ensure R is installed in your system Ensure MS Excel (2007 or 2010)is installed Properly plan your practice SAS Language Lab Hours Be familiar with Case study to be discussed in the class

Post Class Assignment Practice the case study discussed in the class in SAS Language Expected Lab hours required is 5


Day 3 & 4: Linear Regression

7

Linear Regression – Topic Covered

Correlation and Regression Multivariate Linear Regression Theory Coefficient of determination (R2) and Adjusted R2

Model Misspecifications Economic meaning of a Regression Model Bivariate Analysis ANOVA (Analysis of Variance) Multivariate Linear Regression Model Variable identification Response variable exploration

• Distribution analysis • Outlier treatment

Independent variables analyses Heteroskedasticity detection and correction Multicollinearity detection and correction Fitting the regression Model performance check

Case: Multivariate Linear Regression Identify and Quantify the factors responsible for loss amount for an Auto Insurance Company

Domain Covered Insurance Industry

Tool for Practice in Class MS Excel and R

Tool for Self Practice SAS Language (Step wise details will be provided in PowerPoint)




Day 5 & 6: Logistic Regression

8

Logistic Regression – Topics Covered Identifying problems in fitting linear regression on data having “Binary Response” variable Introduction to Generalized Linear Modeling (GLMs) Logistic Regression Theory Logistic Regression Case Variable identification Response variable exploration Independent variables analyses Fitting the regression using SAS language Scoring equation Model diagnostics Analysis of results

• Check for reduction in Deviance/AIC • Model performance check • Actual vs Predicted comparison • Lift/Gains chart and Gini coefficient • K-S stat

Score Card Development

Case: Multivariate Logistic Regression Identify bank customers who will most likely default in making the payment on balance due.

Domain Covered Banking Industry






Day 7: Decision Tree and Clustering

9

Decision Tree & Clustering – Topic Covered Data Mining and Decision Trees Decision Tree Example CHAID analysis Method and Algorithms Running the CHAID analysis and Interpreting the results CART Method and Algorithms Running the CART analysis and Interpreting the results When to use CART and when to use CHAID Defining Clustering Why and Where to use Clustering Clustering methods Clustering examples K-means Clustering Algorithm

Case: CHAID & CART Analysis Identifying the classes of customer having higher default rate

Case: K-means Clustering

Identifying similar groups in database containing auto insurance policy records using K-means Clustering

Domain Covered Insurance and Banking Industry



Pre Class Requirement Ensure R is installed in your system Properly plan your practice SAS Language Lab Hours Be familiar with Case study to be discussed in the class

Post Class Assignment Practice the case study discussed in the class in SAS Language Expected Lab hours require for practice is 5


Day 8: Time Series Modeling

10

Time Series Modeling – Topic Covered Models of time series Moving averages Autoregressive Models The Box-Jenkins model building process Model Estimation Model Validation Model forecasting Identify the ARIMA model Estimate the best ARIMA models Validate the model Forecast the sales based on model

Case : ARIMA Modeling Forecasting future sales based on historical data for an automobile company

Domain Covered Automobile Industry


Pre Class Requirement Ensure R is installed in your system Ensure MS Excel (2007 or 2010)is installed Be familiar with Case study to be discussed in the class

Post Class Assignment Practice the case study discussed in the class in R Expected hours require for practice is 5


Day 9: Logistic Regression

11

Logistic Regression – Topic Covered Identify and develop Dependent variable Perform initial variable reduction and missing value imputation Perform extreme value treatment Prepare correlation matrix and VIF chart Variable reduction through Multicollinearity Perform Binning to prepare modeling dataset Perform sampling to prepare training and validation dataset Run the model Develop report for model outcomes Write the Scoring or implementation strategy

Case: Up-Sell Model Propensity Model for Up-Sell in Telecom Industry

Domain Covered Telecom Industry






Day 10: Market Basket Analysis

12

Association Rule – Topic Covered Affinity analysis to understand purchase behavior Understanding Apriori algorithm Capturing the insightful association available in the transaction records Analysis of output results to plan store layout, promotions and recommendations

Case : Market Basket Analysis Understanding apriori algorithm to identify affinity among the purchase data in the basket based on historical transactions.

Domain Covered Retail Industry


Tool for Demonstration

SAS Language



See you in Class…

© EduPristine – www.edupristine.com

EduPristine

702, Raaj Chambers, Old Nagardas Road, Andheri (E), Mumbai-400 069. INDIA

www.edupristine.com

Ph. +91 22 4211 7474

Swapnil Sawant

[email protected]

+91 9769397264

mailto:[email protected]

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