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8/12/2019 BADM

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PGDM@NMIMS

Batch - 04; Trimester - 04

Course: Business Analytics for

Decision Making

OVERVIEW OF COURSE

Background

o Ours is the age of prolific data. It is said that data doubles every 18 months. Theinternet and the mobile channels through which customers interact withcompanies is perhaps the largest contributor to this data deluge. Another reasonis that corporations invested on technology to improve processes and managecustomer relationships. CRM, ERP, SCM and Core banking applications

generate huge volumes of data. Electronic channels for business like ATM’s andcredit card swipe machines also add their part. The growth of this data hascreated the “Analytics Industry” and sophisticated techniques have becomereadily available to analyze this vast volume of data. Such is the relevance of

Analytics in today’s competitive space that many companies have set up Analytics as a Strategic Business Unit (SBU). Hence, be it the assortment ofproducts on the retail floor, the credit worthiness of banking customers,optimizing the channels of distribution, getting more mileage from onlinepresence or dynamic pricing in airline ticketing, Analytics is increasinglybecoming the key driver of competitive advantage.

o Analytics, as a science, is based on the fundamental principles of Statistical andOR Theory. One cannot analyze huge volume of data with basic technology like

say, MS Excel alone. Tools like SAS, SPSS and the technology of storing data inData warehouses with Oracle or Sybase are a must for handling such volumes.High storage and processing power is becoming increasingly affordable for mostcompanies. All these factors together have contributed to the creation andphenomenal growth of the Analytics especially in India.

Specific Objectives

o To expose students to the nuances of using statistical techniques on voluminousdata for extracting insights that help him make better informed decisions.

o To train students to develop the ability of connecting a business problem to itssolution through an analytical technique

o To educate students to interpret statistical output for business implementation

o Provide hands on exposure to SAS, which is the industry leading software foranalyzing voluminous data

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LEARNING OUTCOMES

The student should be able to understand and appreciate both common and advancedtechniques in Analytics and should be able to apply such techniques to solve businessproblems. He/she will get a handle on those business situations where use of Analyticscan become a powerful differentiator. The participant should also become comfortable inusing the SAS software and should be able to apply basic Statistical theory tounderstand and interpret SAS output and communicate using business parlance.

SCOPE OF THE COURSE

The study plan covering readings/handouts, classroom sessions, Group Work sessionsand Assignments corresponding to each session are outlined below.

Quizzes or surprise tests would be conducted on the same. For post contact reading,please refer to the handouts that will be provided at the end of each session.

In addition, there will be computer lab sessions on SAS - several sessions will bededicated for these hands-on exercises in the computer lab

The course is spread over 20 sessions. Each session will be of 80 minutes. Thefollowing broad topics will be covered in the course:

o Introduction to Analytics & Exploratory Analysis

o Linear Models using Regressiono Classification techniques

o Binary models using Logistic Regression

o Common analytic techniques

PEDAGOGIC TOOLS

The pedagogic tools available to each participant are:

Study Hand-outs

Study Plan Folder

SAS for hands-on exercises Reference Books

Statistics for Business and Economics by Anderson, Sweeney and Williams

Multivariate Analysis by Anderson Black and Hair

Business Forecasting by John Hanke

Competing for Analytics by Thomas Davenport

Statistics for Management by Levin and Rubin

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The study encompasses the following:

Classroom Lectures and Interactions

Reading of Handouts being provided

Case Analysis and submission of assignments

SAS sessions in the Lab

GRADING

Data Analysis Exercises - 10%

Assignments - 20%

Projects/ Presentations - 20%

End Term Exam - 50%

Please come prepared w ith the handou t readings as speci f ied for each session

and br ing th is study plan boo k let in c lass

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Decision Making

SESSION 1

INTRODUCTION TO ANALYTICS

Readings

Competing on Analytics

Classroom Session

Analytics – Definition and Scope

Impetus for growth of Analytics

Applications in Business Skill sets Required

Tools for Analysis

SESSION 2

ANALYTICS PROCESS

Readings

Analytics Process

Class Room Session Business Intelligence and Analytics

Business Issues leading to Analytics

Stages of Analytics

SESSIONS 3 & 4

ANALYTIC PROCESS: BASICS OF STATISTICS

Readings

Statistics for Business and Economics by Anderson, Sweeney and Williams(Chapters – 9,10,12,13)

Class Room Session

Data types and formats

Some Basic Computations

Median, Mode, Standard Deviance, Variance, Skewness, Kurtosis

Hypothesis tests – t-test / ANOVA / Chi-Square

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SESSION 5

ANALYTIC PROCESS: DATA CLEANING

Readings

A Review of Missing Data Treatment Methods

Outliers detection and Treatment

Data cleaning and preparation

Class Room Session

Data quality issues

Treating missing values

Treating outliers

SESSIONS 6 & 7

ANALYTIC PROCESS: EXPLORATORY ANALYSIS

Readings

Exploratory data analysis

Class Room Session

Objectives of Exploratory Analysis

Methods and techniques applied

Bivariate analysis for exploratory analysis

Presentation techniques

SESSION 8

BASICS OF STATISTICAL MODELING

Readings

Mathematical Modeling

Class Room Session

What is a mathematical model?

Types and methods of development

Applications

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SESSION 9

LINEAR MODELS – SIMPLE REGRESSION AND CORRELATION

Readings

Statistics for Business and Economics by Anderson, Sweeney andWilliams(Chapter – 14)

Classroom Session

Linear Models

Estimation using least squares

Measures of regression

SESSION 10

PREPARING DATA FOR ANALYSIS

Readings

Data cleaning and preparation

Classroom Session Categorical variables

Time orientation

Aggregation/ Ratios

SESSIONS 11 & 12

LINEAR MODELS – MULTIPLE REGRESSION AND MODEL BUILDING

STRATEGIES

Readings

Statistics for Business and Economics by Anderson, Sweeney andWilliams(Chapter – 15-16)

Case:- Luminar Insights

Classroom Session

Linear and Non Linear Models

Transformations (power, exponential, logistic, log and ztransforms)

Elasticity, Contributions and Diminishing returns

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SESSSION 12 & 13

CLASSIFICATION TECHNIQUES

Readings

CHAID Analysis

SPSS Decision Tree

Case:- Breaking Barriers: Micro-Mortgage Analytics

Classroom Session

Chi-Square Automatic Interaction Techniques (CHAID)

Decision Trees

SESSSIONS 14 to 16

BINARY MODELS – LOGISTIC REGRESSION

Readings

Statistics for Business and Economics by Anderson, Sweeney and Williams (Chapter – 15.9)

Logistic Regression Analysis (C. Mitchell Dayton)

Classroom Session

Logistic Regression

Evaluation of Results (Lift Chart etc.)

SESSSIONS 17 to 20

COMMON ANALYTIC TECHNIQUES

Readings

A toolkit for Analyst Case: Harrah's entertainment Inc.

Classroom Session

Recency, Frequency, Monetary Modeling

Market Basket Analysis

Water Fall Diagrams

Pareto Analysis

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