PGDM Business Analytics Duration of the Course: 2 Years ... · PGDM – Business Analytics Duration of the Course: 2 Years Full-time (4 semesters) ... IBM, Accenture, Infosys have

Post Graduate Diploma In Management - Business Analytics St. Teresa’s College (Autonomous), Ernakulam

1 Curriculum & Syllabus – 2015 Admission onwards.

PGDM – Business Analytics

Duration of the Course: 2 Years Full-time (4 semesters)

Preamble

Following document contains the semester wise course matrices, detailed syllabuses along

with credits, breakup of credits in terms of lecture, tutorials and laboratory, assessment pattern,

competencies of the courses and sample problems. Specific mention of two projects is also

envisaged in the document.

Graduate Attributes

Need of the PGDM- Business Analytics programme.

Most industry sectors have recognized the value that Business Analytics can provide in not

only driving Business Solutions, but also in helping them to differentiate themselves to

customers, investors and regulators. The global Business Analytics is a USD 105 billion

market, growing at a CAGR of 8%. India, with its surfeit of talent, has become the Analytics

hub for organizations across the world. Large corporate like Walmart, Target, Citibank, ICICI

Bank, Airtel, Vodafone are increasingly adopting analytics in their processes. Consulting

giants like PwC, IBM, Accenture, Infosys have large teams offering Analytics solutions to

their clients. All of these translate into a huge global and domestic demand for Business

Analytics professionals.

Employment Potential

As per McKinsey Global Institute, the analytics Industry is one of the fastest growing in

modern times which is poised to become a $50 billion market by 2017. With this sudden surge

in the analytics industry there is a tremendous increase in the demand for analytics expertise

across all domains, throughout all major organizations across the globe. It has been predicted

that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with

deep analytical skills as well as 1.5 million managers and analysts with the know-how to use

the analysis of big data to make effective decisions.

NASSCOM has estimated that from 50,000 today, the demand for Analytics professionals in

India will grow to 2,50,000 in the coming two three years.

According to Analytics India Magazine (2013), India will remain the preferred destination for

Analytics Outsourcing as compared to other Asian countries like Philippines and China.

Unlike the BPO‘s, Analytics (considered as part of KPO) requires skills that are not easily



available in these countries. India’s Analytics talent pool will be in high demand because

of their process expertise and English language proficiency.

Major Employers in Business Analytics

There are mainly four types of employers

1. Large IT companies who have an Analytics practice [Example: Accenture, Infosys,

TCS, Wipro]

2. Analytics KPOs [Example: Genpact, WNS]

3. In-House Analytics Units of large corporate [Example: Citibank, Dell, HP, Spencer,

Target]

4. Niche Analytics firms [Example: Cognizant Analytics, AbsolutData, Mu Sigma

Analytics]

Career Opportunities

After learning this programme, student can have following roles in the industry.

Analytics Analyst

Data Analyst / Data Management Analyst

Research Analyst

Reporting & Analytics Lead

Data Scientist

Predictive Analytics Specialist

Data visualizers

Business Analytics Consultant

Data Miner Analytics Manager

Predictive Modelling Analyst

Statistician

Board of Advisors

The programme has been designed under the prudent guidance of following eminent

professionals from industry and academia.

Dr. (Prof) Prithvi Pal Yadav:

A Distinguish Professor of IIM with professional experience of more than two and half

decades.

At present, he is a Director, BIMTECH, Bhubaneswar.

Ex – Acting Director, IIM; Ex- BoG Member, IIM; Member – Senate, IIT; Ex Director,

GHSIMR, Kanpur

Member of BoG, IIM Indore, Subject Matter Expert UPSC, Govt of India



Teaching premiere institutions in India like IITs, IIMs and foreign universities.

An expert in Data Mining, Business Intelligence Solutions, Six Sigma Applications

and SPSS.

Dr. S.R. Kulkarni:

Ph. D in Statistics; M. Phil in Statistics.

Distinguished Data Scientist at [24]7 Inc., Innovation labs at Bangalore (July 3rd 2011-

Till date); Scientist - Analytics at DataInfoCom (Nov, 2009 to 2012); Manager -

Statistical Research at Cranes Software International Ltd., Bangalore, India (2003 –

2009).

Analytics Experience:

Mining of e-commerce Data

Patents on User Behaviour on websites

Analysis and Design of Experiments(DOE)

Predictive Decision Management Software System for Offline data Mining

Development of algorithms for advanced statistical techniques

Sample surveys and analysis

Dr. Shesha Shah:

MS and PhD in System Science

Co-Founder and Chief Data Scientist at Analytics

Working with global giants, Yahoo, Travelocity and Dell computers in areas of

business intelligence.

18 years of industry experience in the areas of advanced analytics.

Global work experience

Dell Innovator award, 2012

10 Technology Patents

Domain expert in Pattern Recognition, Parallel and distributed algorithms, web data

mining, Statistics, Predictive Modelling, NLP and text processing, Information

retrieval etc.

Dr. (Prof) Nishat:

Base SAS and Stat; Advance SAS, Report and Graph; Advanced Post-Graduate

Diploma in Clinical Research; B.D.S.

5+ years industrial experience in analytics, applied statistics, clinical research.

Given seminars, paper presentation and conducted workshops at University Of New

York, USA and University Of Virginia, USA

Worked intensively in all aspects of SAS including data analysis, database validation;

data cleaning, reporting and applied statistics.

Been teaching since 8 years in various fields of SAS, medical and clinical analytics

subjects.



Curriculum Design of Specialized Two-Year Post Graduate Programme

The programme will train the students in two specific domains. First, they will understand

basic management concepts related to marketing, finance and entrepreneurship and secondly

they will attain the special skill sets for applying Analytics in the management areas.

1. Programme Outcome

The programme will develop a deeper sense about management principles and techniques in

the field of marketing, finance, quantitative analysis and entrepreneurship. They will also

equip themselves with adaptive thinking which is applicable in the management domain.

Students will learn special skill sets for application of Business Analytics in the field of

management. Computational skills are vigorously focussed in the programme.

A reasonable mix of common courses with core courses will make them self-directed on the

path of continuous learning. Core courses will make them capable to apply analytics in

specific areas and then to effectively communicate to the stake holders of given management

challenge.

A good Data Scientist or Business Analyst is supposed to work effectively and efficiently in

the individual capacity and as a team. The programme delivery will in-built these skills and

aptitude.

Students will get the updated knowledge and developments in the field of Analytics by virtue

of interactions with learned industry professionals and academicians.

2. Stages of Curriculum Design

Following section covers the Vision and Mission of the department and that of the institute,

Context of programme and Credits of PG Programme.

2.1 Vision and Mission

Vision of the Department

To be the centre of Commerce and Management Education.

Mission of the Institute

Moulding empowered, committed and socially responsible women leaders.



Objective of the Programme

By virtue of massively enhanced data storage and data retrieval capacity due to technological

advancements in the field of Information Technology, possibility of probing extensively into

the past business information or data has paved the way of new specialized domain called

Business Analytics or Big Data Analytics. Further, the demand of Data Scientists in India and

the World has reached to a level which is difficult to meet through the existing pool of

knowledgeable professionals in this field. This programme is an holistic attempt to create Data

Analysts or Business Analysts for bridging the gap.

Detailed Distribution of Courses and Credits of PG Diploma Program

Sem

este

r

Course Code Title of the Course #

Hrs/Week

# C

red

its

Tota

l Hrs

/

Sem

Exam

Du

rati

on

SA FA

1 CO1B01TM Management Concepts and Practices 4 3 72 3 25 75

CO1B02TM Marketing Management 4 3 72 3 25 75

CO1B03TM Accounting for Managers 4 3 72 3 25 75

CO1B04TM DBMS and Data Warehousing 4 3 72 3 25 75

CO1B05TM Spreadsheet Modelling and Decision Analysis 4 4 72 3 50 50

CO1B06TM Statistics for Business 5 4 90 3 50 50

2 CO2B07TM

Financial Markets and

Instruments 4 3 72 3 25 75

CO2B08TM

Introduction to

Entrepreneurship 4 3 72 3 25 75

CO2B09TM

Management Decision

Science 4 3 72 3 50 50

CO2B10TM Business Finance 4 3 72 3 25 75

CO2B11TM

Introduction to Business

Analytics 5 4 90 3 25 75

CO2B12TM

SPSS for Research

Methodology 4 4 72 3 50 50

3 CO3B13TM

Consumer Behaviour and

CRM 4 3 72 3 25 75

CO3B14TM

Economic Analysis for

Business Decisions 5 3 90 3 25 75

CO3B15TM Financial Modelling 5 3 90 3 50 50

CO3B16TM

Multivariate Data Analysis

– 1 4 3 72 3 50 50

CO3B17TM Time Series Econometrics 5 4 90 3 50 50



CO3B01PM Project I 2 4 36 3 30

50

(P)

4 CO4B18TM

Predictive Modelling using

SAS 5 3 90 3 50 50

CO4B19TM Big Data Analytics 5 3 90 3 50 50

CO4B20TM

Multivariate Data Analysis

– 2 5 3 90 3 50 50

CO4B21TM Analytics with R 4 3 72 3 50 50

CO4B22TM

Data Mining for Business

Analytics 4 4 72 3 50 50

CO4B01PM Project II 2 4 36 3 60

100

(P)

40

(V)

Project

All

The programme is of 80 credits with a definition of 15 contact hours per credit.

3.3 Credit Distribution

S. No. Course Work-Subject Area Credits

1 Core Courses 72

3 Projects (two in numbers) 08

Total 80

3.4 Core Courses

There are 22 Core courses which includes 10 specialized Core courses.

Core specialized courses are 10 which take students from the Introduction of Business

Analytics to Data Mining for Business Analytics.

A Core Courses Semester Area Numbers

1

Management Concepts and

Practices I

General

Management 1

2 Marketing Management II Marketing

Management

1

3 Consumer Behaviour and CRM III 2

4 Statistics for Business I Statistics 1

5 Accounting for Managers I Accounting 1



6

Financial Markets and

Instruments II Finance 1

7 Business Finance II 2

8

Economic Analysis for Business

Decisions III Economics

1

9 Introduction to Entrepreneurship II Entrepreneurship

10

Spreadsheet Modelling and

Decision Analysis I Quantitative

Techniques

1

11 Management Decision Science II 2

12 Financial Modelling IV 3

B Core courses (Specialized)

13 DBMS and Data Warehousing I

Analytics

1

14

Introduction to Business

Analytics II 2

15 SPSS for Research Methodology II 3

16 Analytics with R III 4

17 Multivariate Data Analysis – 1 III 5

18 Time Series Econometrics III 6

19 Predictive Modelling using SAS IV 7

20 Big Data Analytics IV 8

21 Multivariate Data Analysis – 2 IV 9

22

Data Mining for Business

Analytics IV 10

4 Examinations

Following section explains the assessment pattern for two types of courses. One type is

those courses which are largely conceptual like Management Concepts and Practices and

Marketing Management. The other type of courses in this programme is quantitative and

application oriented courses like Management Decision Science and Analytics with R.

For conceptual courses, 25 and 75 weights for Sessional Assessment and University Exam

has been designed respectively.

The weights of Class Test (one only) and that of two Assignments are shown below.

Item Weightage

Class Test 1 5

Assignment 1 10

Assignment 2 10



For quantitative courses, 50 and 50 weights for Sessional Assessment and University

Exam has been designed respectively. This is designed for accommodating more

supervised learning of students.

The weights of Class Tests (two) and that of two Assignments are shown below.

Item Weightage

Class Test 1 and 2 5 + 5 =10

Assignment 1 20

Assignment 2 20



SYLLABI OF CORE COURSES OF BUSIINESS ANALYTICS

(Only Core Courses - The curriculum is designed to expose students to facets of Management

– Marketing, Finance and Entrepreneurship with strong skills in Business Analytics.)



SEMESTER 1

Course Code: CO1B01TM

Name of the Course: Management Concepts and Practices

Duration: One Semester

Semester : 1

Total Lecture Hours: 45 Aim of the Course: The course helps to focus on the concepts and methods of managing an organization. It helps to understand the functioning of an organization, understand the various techniques involved in solving managerial problems. This module is designed

to (i) introduce management and organizational concepts and related theories; (ii) examine the

formulation and implementation of business strategy; and (iii) explain the main management

issues in marketing, human resources, operations and sustainability.

Course Overview and Context: The course gives a brief idea of the nature and characteristics of management and how management functions in an organization. The course tells about the need for planning and also talks about the importance of forecasting and decision making in an organization. It helps in understanding the various types of decision making techniques that managers make use of in taking an important management decision. Syllabus Content Unit 1 11 hours Introduction, Definition of Management, Nature and Characteristics of Management, Management as an Art, Science and Profession , Management and Administration, Levels of Management, Significance and Objectives of Management, Social Responsibility of Management , Management by Objectives (MBO).

Unit 2 9 hours

Fundamentals of Planning, Need for Planning, Definitions of Planning, Planning Process, Components and Types of Planning.

Unit 3 9 hours Forecasting and Decision Making, Importance of Forecasting and Planning, Definitions of Decision Making, Types of Decision Making, Decision Making Techniques, Decision Making Approaches. Unit 4 9 hours Managing Diversity, Meaning and Reasons for Diversity, Managing Diversity in Organizations, Benchmarking Concepts (performance benchmark).



Unit 5 7 hours Case Analyses to illustrate the various aspects of management theory – global as well as

Indian; multiple situational analyses; demonstrating different challenges and

opportunities.

Competencies of the course C1. Define management and understanding the characteristics of management.

C2. Describe the different levels of management and also understand the significance and

objectives of management.

C3. Understanding the concept of Social Responsibility of Management.

C4. Understanding the fundamentals of planning as well as the various types of Planning.

C5. Identify the need for planning and describe the planning process.

C6. Understanding the concept of Decision making

C7. Recognize the importance of Forecasting and Decision making.

C8. Understand the various types of Decision Making techniques along with Decision making

approaches

C9. Understanding the term Diversity.

C10. Understand the various reasons for diversity.

C11. Understand the Benchmarking Concepts and the art of managing diversity in

organizations.

C12. Understanding management theory with respect to Global and Indian Context.

C13. Identify the various challenges and opportunities in management theory.

Learning Resources

Textbook

1. Rudani, B.R. (2011). Management and Organizational Behaviour. New Delhi: Tata

McGraw-Hill.

References

1. Drucker, P.F. (2009). The Practice of Management. New Delhi: Allied Publishers.



2. Massie, L.J. (1979). Essentials of Management. New Delhi: Prentice - Hall of India

Private Limited.

3. Tripathi, P.C., Reddy, P.N. (1991). Principles of Management. New Delhi: Tata

McGraw-Hill Publishing Company Limited.

Model Question Paper

PGDM- Business Analytics CBCSS Exam

First Semester

CO1B01TM - Management Concepts and Practices

Time: 03 Hours Maximum Marks: 75

Section – A

Answer any 5 Questions. Each Question carries 2 Marks (5 × 2 = 10)

Q1. Define the term Management.

Q2. What is corporate social Responsibility?

Q3. Differentiate between goals and plans.

Q4. What do you mean by Managerial Competencies?

Q5. What are the requirements of good Plan?

Q6. What is meant by Cultural Diversity?

Q7. Identify at least 3 major challenges global manager would face in Multinational

corporations

Q8. What is Business Ethics?

Section – B

Answer any 5 questions. Each question carries 4 marks (5 × 4 = 20)

Q1. ―A successful enterprise has to achieve its goals effectively and

efficiently‖. Explain.

Q2. Differentiate between Manager and Administrator.

Q3. What is workforce diversity, and what implications does it have for

managers?

Q4. What is Decision making? What are the steps of Decision making?

Q5. What is the relationship between planning and organizational performance?

Q6. How is globalization affecting the way managers do their jobs?

Q7. Explain the functions of Management with appropriate example.

Q8. Explain Brain storming.

Section – C

Answer any 3 questions. Each question carries 15 Marks (3 × 15 = 45)

Q1. Define SWOT Analysis with an example and explain how it is helpful

for initial stage of strategic planning.

Q2. Discuss the contribution of Maslow`s Hierarchy of motivation

Q3. What are the arguments in favour of Social responsibility of Business , Discuss

Q4. What is MBO? Explain the steps involved in MBO.

Q5. What is Decision Tree? Explain.

Q6. Case Study:



Human Relations in the office

In the personnel office of the Tripti Manufacturing company, two girls, priya and Mary

disliked each other. This fact was known to the personal manager, who had observed that the

girls quarreled upon occasion. Although this lack of harmony in the office disturbed the

personnel manager, he did not consider the situation serious enough to warrant anything more

than a word of caution to the girls now and then. However as time went on the situation

became worse. Quarrels became more and more frequent each girl tried to hinder the other in

her work. They openly refused to cooperate with each other and began to lose a great deal of

time by going out of their way to interfere with each other`s work. One day, while the

personnel manager was sitting quietly at work, priya rushed into his office, burst into tears and

exclaimed that she could no longer continue to work unless Mary was transferred or

discharged.

Questions:

1. If you were the personnel manager what would you do, and Why?

2. What do you think could have been done to prevent this situation from

happening?

Blue Print - Question Paper

Module * Part A Part B Part C

1 2 1 1

2 1 2 2

3 2 2 1

4 2 1 1

5 1 2 1



Course Code: CO1B02TM

Name of the Course: Marketing Management


Semester - 1 Aim of the Course: The aim of the course is to provide an introduction to marketing and understanding its various aspects. The course is designed to serve as an introduction to the theory and practice of marketing. The course also helps students to apply and understand major marketing concepts market segmentation, customer value preposition. Course Overview and Context: The course gives a brief understanding marketing concepts and their application to profit-oriented and non-profit oriented organizations. This course provides an introduction to all aspects of marketing, including strategic marketing planning, marketing research, product planning and development. It provides an understanding of the competitor’s strategies, Characteristics and Classification of products etc. Syllabus Content Unit 1 10 hours Defining Marketing in the Contemporary Context Marketing Concept, Importance of marketing, Scope of marketing, Changes in marketing management in the 21st Century, Tasks necessary for successful marketing management. Developing Marketing Strategies and Plans Customer Value Preposition, Strategic planning process and levels – Corporate planning, Strategic Business Unit planning, Marketing Plan. Components of a modern Marketing Information System, Key methods for tracking and identifying opportunities in the macro-environment, Some important macro-environment developments. Concepts and benefits of Market Segmentation, Variables of Market Segmentation, Identification of appropriate target market for given segment, The positioning strategies of companies’ on the basis of product differentiation. Unit 2 8 hours Competitive Dynamics Analysis of competitors’ strategies, objectives, strengths, and weaknesses, Strategies of market leaders to expand the total market and defend market share, How market challengers attack market leaders.



Unit 3 9 hours Setting Product Strategy Characteristics and Classification of products, Management of product mix and product lines, Packaging, labeling, warranties, and guarantees as marketing tools. Pricing Strategies for Products and services, Adapting prices to meet varying circumstances and opportunities, Initiating a price change, Responding to a competitor’s price challenge. Unit 4 10 hours Designing and Managing Integrated Marketing Channels Designing a Marketing Channel system and value network, Integration of channels and management of channel conflict, Future of e-commerce. Role of Marketing communications, Major steps in developing effective communications, Concept of Communications Mix, Integrated Marketing Communications program. Challenges in developing new products, Organizational structures to manage new-product development, Stages in developing new products, Concept of Diffusion of Innovation. Unit 5 8 hours The Marketing Environment The Emerging Market Environment and implications for Marketing Strategy, Marketing Mix, Marketing Communications, Marketing Budgetting and Marketing ROI’s. Current trends in defining and describing Market Opportunities, Competition, Channels, Media and strategies to monitor, anticipate, manage and drive these for the benefit of our businesses. Competencies of the course C1. Understanding the definition of marketing and the scope of marketing.

C2. Understanding the nature of marketing in the 21st century.

C3. Familiarize with various marketing strategies such as customer value preposition, strategic

planning process.

C4. Understanding the Components of a modern Marketing Information System.

C5. Describe important macro-environment developments.

C6. Understanding the concept of Decision making

C7. Understanding the concept of market segmentation.



C8. Describe the positioning strategies of companies‘ on the basis of product differentiation

C9. Understanding competitors‘ strategies, objectives, strengths, and weaknesses.

C10. Identify the characteristics of products and its classification.

C11. Understand product mix and various product lines.

C12. Recognize the pricing Strategies for Products and services.

C13. Understanding Marketing Channel system and value network.

C14. Understanding the concepts of marketing mix, marketing budgeting, and marketing

return on investments.

C15. Understanding Current trends in defining and describing Market Opportunities,

Competition, Channels, Media.

Learning Resources:

Kotler, P., Keller, K. L. , Koshy, A., Jha, M. (2013). Marketing Management: A South

Asian Perspective. India: Pearson Education.

Rajan, S. (2005). Marketing Management. India: Tata McGraw-Hill Education.

Karunakaran, K. (2013). Marketing Management. India: Himalaya Publishing

House.

Kumar, A., Meenakshi. (2013). Marketing Management. India: Vikas Publishing

House Pvt Ltd.

Ramaswamy, V. S., Namakumari, S. (2009). Marketing Management Global

Perspective, Indian Context. India: Macmillan India Limited.


Module * Part A Part B Part C 1 2 1 1 2 1 2 2 3 2 2 1 4 2 1 1 5 1 2 1



PGDM- Business Analytics – CBCSS Exam

First Semester

CO1B02TM - Subject: Marketing Management

Time: 3 hours Maximum marks: 75

Section – A


1. Define marketing.

2. What are the five P‘s of marketing?

3. Define segmentation.

4. Define customer value proposition.

5. What is marketing information system?

6. Define market share.

7. What is a product?

8. Define packaging.

Section – B

Answer any 5 questions. Each question carries 4 marks (5 × 4 =20)

1. Describe the need for market segmentation.

2. Describe marketing mix.

3. What are the components of strategic market planning?

4. What are the stages in decision making process?

5. Discuss the reasons for the need of MIS.

6. What are the benefits of market segmentation?

7. How products are classified?

8. What are the different types of labeling?



Section – C


1. Describe the components of Marketing Information System.

2. Discuss the different types of Market Segmentation.

3. How to determine strength and weakness of competitors?

4. What is SWOT analysis?

5. Elaborate the strategies of market leaders.

6. What are the different types of product pricing?



Course Code : CO1B03TM Name of the Course: Accounting for Managers


Semester - 1

Aim of the Course: The aim of this Course is to familiarise the students with the management

accounting concepts, the financial accounting process, preparation of final accounts/reports,

interpretation of financial statements, sources uses of funds, financial analyses, cost analysis

and their applications in managerial decision making, budget planning and its control,

different types of ratios and its application.

Course Overview and Context: Accountants play a key role in the formulation and

implementation of business decisions, both strategic and tactical, through their uniquely

analytical approach to the assessment of the financial implications of business plans.

Accounting for Management focuses on the crucial role which financial information plays in

decisions made by management teams, and in the monitoring and planning of a business

organisation's objectives. The course seeks to cover accounting studies to a more refined level,

similar to professional qualification standard along with retaining theoretical analysis.

Syllabus Content

Unit 1

Conceptual Framework 8 hours

Double-entry book-keeping system, journal, ledger, posting, debits, credits, trial balance,

adjusting entries, final accounts for non-corporates (manufacturing trading, PandL, B/S),

company final accounts, computerised accounting.

Unit 2

Financial Statement Analysis and Interpretation 10 hours

Financial statement analysis and interpretation, ratio analysis, fund flow analysis. Cash flow

analysis.

Unit 3

Budgets and Budgetary Control 7 hours

Concepts of budget and its advantages, functional, master and cash budget, flexible and zero

based budgeting, responsibility accounting, performance budgeting.

Unit 4



Marginal Costing and Profit Planning 12 hours

Absorption costing and marginal costing, direct costing, contribution, profit planning, Cost-

volume-profit Analysis (CVP analysis), Break-even Analysis, Break-even charts, advantages

and limitations of marginal costing, application of marginal costing technique, decisions

involving alternative choices, determination of sales mix, make or buy decision, exploring

new markets, discontinuance of a product line. Standard costing as a tool for control, variance

analysis.

Unit 5

Depreciation Accounting and Inventory Valuation 8 hours

Concept of depreciation, methods of depreciation, objectives of inventory valuation, methods

of inventory valuation.

Competencies of the course:

C1. Understand the Conceptual Framework of Principles of accounting.

C2. Understand the Double-entry book-keeping system.

C3. Understand the books of original entry such as Journal & the purpose of such books.

C4. Understand the process of recording transactions in ledger.

C5. Identify the link between Journal & ledger.

C6. Characterise the techniques of posting, debits, credits & analyze the need of trial balance.

C7. Recognise the process of adjusting entries, and preparation of final accounts for non-

corporates.

C8. Understand the Analysis of Financial Statement and Interpretation.

C9. Identify the importance of ratio analysis, fund flow analysis & Cash flow analysis.

C10. Understand the Concepts & purpose of Budgets and Budgetary Control.

C11.Understand the responsibility accounting & performance budgeting.

C12. Understand the Marginal Costing and Profit Planning through Cost-volume-profit

Analysis (CVP analysis), Break-even Analysis, Break-even charts.

C13. Determination of sales mix, make or buy decision, exploring new markets,

discontinuance of a product line.



C14. Identify the need of standard costing as a tool for control & variance analysis.

C15. Understand the Concept of depreciation, methods of depreciation, objectives of

inventory valuation, methods of inventory valuation.

Learning Resources:

Khan, M. Y. and P. K. Jain (2013). Management Accounting - Text, Problems and Cases.

New Delhi: McGraw Hill Education.

Maheshwari, S. K., S. N. Maheshwari and Sharad K. Maheshwari. (2012). Accounting for

Management. New Delhi: Vikas Publishing House.

Gupta, Ambarish. (2012). Financial Accounting for Management: An Analytical

Perspective. New Delhi: Pearson Education.

Banerjee, Ashok. (2009). Financial Accounting: A Managerial Emphasis. New Delhi:

Excel Books.

Horngren, Charles T., George Foster and Srikant Datar. (2007). Cost Accounting: A

Managerial Emphasis. New Delhi: Prentice Hall

Alexander, David and Anne Britton. (1996). Financial Reporting. Boston: International

Thomson Business Press.


Module * Part A Part B Part C

1 2 1 1

2 1 2 2

3 2 2 1

4 2 1 1

5 1 2 1

PGDM- Business Analytics – CBCSS Exam First Semester

CO1B03TM - Accounting for Managers


Section A


Q1. What do you mean by Preliminary expenses?

Q2. What is equating the accounting equation?

Q3. State the objectives of preparing a Trial Balance.

Q4. Give the formulae for calculating cost of goods sold?

Q5. Give the meaning of Interim dividend?

Q6. Define a budget?

Q7. What is a flexible budget?

Q8. What is P/V ratio?

Section B




Q1. How are accounts classified? Briefly explain the rules of personal, real & nominal

accounts.

Q2. From the following information, prepare a comparative income statement.

Particulars 31.3.2012 31.3.2013

Sales 2000000 1600000

Cost of goods sold 1200000 800000

Administration expenses 200000 140000

Selling and Distribution

expenses 200000 140000

Other incomes 80000 40000

Income tax 240000 240000

Q3. From the following information calculate: a) Gross Profit Ratio; b) Net Operating Profit

Ratio; c) Net Profit Ratio

Net Sales 5,00,000

Cost of goods sold 3,50,000

Selling expenses 12,000

Administrative expenses 8,000

Interest Income 5,000

Loss on the sale of old machine 12,000

Q4. R. K. Exports Ltd. Wishes to arrange overdraft facilities with its bankers during the period

April-June 2012 when it will be manufacturing mostly for stocks. Prepare a cash budget for

this period from the following data, indicating the extent of the bank facilities the company

will require at the end of each month.

Period Sales Purchases Wages

Feb 2012 180000 124000 12000

March 2012 192000 144000 14000

April 2012 108000 243000 11000

May 2012 174000 246000 10000

June 2012 126000 268000 15000

50 percent of the sales are realised in the month following the sales and the remaining 50

percent in the second month following. Creditors are paid in the month following the month of

purchase. Cash at bank on 1st April 2012 is Rs.25,000.

Q5.The following data is given

Fixed cost Rs.8,000

Profit earned Rs.2,000

Break-even sales Rs.40,000

Calculate the actual sales

Q6. From the following data, you are required to estimate the value of stock at the end of the

financial year 31st March 2012 as per AS 2:

Categories

of Stock

Cost of raw

materials (Rs)

Attributable

works cost

(Rs)

Attributable

selling overheads

(Rs)

Expected

market

price

Item No.P 42100 8,400 3200 50800



Item No.Q 28900 10,700 3600 51800

Q7. What is meant by CVP analysis? Explain.

Q8. State any two causes of depreciation. Give the meaning of diminishing balance method of

depreciation.

Section C


Q1. Explain the accounting concepts and convention.

Q2. Draw up a flexible budget for overhead expenses on the basis of the following data and

determine the overhead rates at 70%, 80% and 90% plant capacity.

Particulars

at 80%

capacity

Rs.

Variable Overheads

Indirect Labour 12000

Stores including spares 4000

Semi-variable overheads:

Power (30% fixed, 70% variable) 20000

Repairs and maintence (60% fixed,

40% variable) 2000

Fixed Overheads:

Depreciation 11000

Insurance 3000

Salaries 10000

Total Overheads 62000

Estimated direct labour hours 124000 hrs

Q3. You are given the following data for the year 20012 of XYZ Co.Ltd

Variable Cost 6,00,000

Fixed Cost 3,00,000

Net Profit 1,00,000

Total 10,00,000

Find out a) Break-even point b) P/V ratio, and c) Margin of safety.Also draw a break even

chart showing contribution.

Q4. A Ltd. purchased the following machines:

On 1 Jan 2010 Rs.40,000

On 1 July 2011 Rs.20,000

On 1 Oct 2012 Rs.10,000

Depreciation was provided at 10%.p.a under the diminishing balance method. The

machine purchased on 1 July 2011 was sold on 31s March 2012 at Rs.15,000. Show the



necessary ledger accounts in the books of A Ltd. Assuming that the accounts are closed on 31st

December each year.

Q5. Prepare a Cash Flow statement from the following Balance Sheets of Gujarat Spinning

Mills Ltd.

Liabilities 2011 2012 Assets 2011 2012

Share Capital 400000 500000 Machinery 500000 700000

General Reserve 125000 135000

Long Term

Investment 70000 56000

Profit & Loss

A/c 110000 190000 Stock 210000 280000

12% Debentures 200000 150000 Debtors 140000 114000

14% Mortgage

Loan 100000 160000 Cash 20000 40000

Creditors 80000 95000 Bank 50000 20000

Share Issue

Expenses 25000 20000

1015000 1230000 1015000 1230000

Total interest paid during the year amounted to Rs.37,800.

Q6. Explain Break-even analysis. State the uses of break-even chart.



Course Code : CO1B04TM

Name of the Course: DBMS and Data Warehousing Duration: One Semester Semester - 1

Aim of the Course: The subject gives an understanding on how technology and architecture collaborate together to serve the business requirements of different users in the organization. This subject enables the students to the fundamentals of Data Warehousing and Databases, and how to leverage them for maximising individual and organisational effectiveness. Course Overview and Context: The course predominantly explains the importance of data management in the organization for transactional and analytical objective. This course focuses on the fundamentals of Data Warehousing and Databases. The operational data from database management systems are used for day-to-day business transactions and serve the users on current information specifically whereas the goal of Data Warehousing is to provide the users with homogenized, consistent and comprehensive view of the organization to support planning, forecasting and decision making processes at an enterprise level.

Syllabus Content

Unit 1 12 hours Introduction to Database Management Systems Introduction to databases, the relational model, database design, normalization process, parallel and distributed databases, object oriented databases: concept, web technology and DBMS, transaction management. Unit 2 9 hours Introduction to Data Warehousing Data warehousing concepts, Databases versus Data Warehousing, Business need for data warehousing, architecture of Data Warehouse, building a Data Warehouse, properties of data in Data Warehouse, importance of Meta Data, Data Marts, critical success factors of Data Warehouse, trends in Data Warehousing. Unit 3 9 hours Data Preparation for Data Warehousing Mapping Data Warehouse with Business, dimensional modeling, Data Extraction, Transformation and Loading Tools (ETL), importance of data quality in Data Warehousing.



Unit 4 9 hours Data Warehouse and Analysis Categorizing users of Data Warehouse and their business requirement, reporting and query tools, On-Line Analytical Processing (OLAP) in Data Warehouse, role of Data Warehousing on web applications, introduction to Data Mining, Data Visualization. Unit 5 6 hours Data Warehouse Implementation and Maintenance Introduction to Business Intelligence Applications, expanding Data Warehouse/ Business Intelligence System, deployment, growth and maintenance of Data Warehouse.

Competencies of the course C1. Understand the basics of databases and how to design databases.

C2. Understand transaction management and how it is useful in database management.

C3. Understand the concept of web technology and how it helps in managing databases.

C4. Distinguish databases from data warehousing.

C5. Understand the need for data warehousing by various businesses.

C6. Understand the architecture of Data Warehouse.

C7. Understand the properties of data in Data Warehouse.

C8. Understand the importance of Meta Data and Data Marts.

C9. Identify the critical success factors of Data Warehouse.

C10. Understand the mapping of Data Warehouse with business.

C11. Understand the various applications of Business Intelligence.

C12. Categorize the users of Data Warehouse and their business requirement.

C13. Understand On-Line Analytical Processing (OLAP) in Data Warehouse.

C14. Understand the role of Data Warehousing on web applications.

C15. Understand the importance of data quality in Data Warehousing.

Learning Resources

Mundy, Joy. et al. (2008). The Data Warehouse Lifecycle Toolkit. Indianapolis:

Wiley publishing Inc.

Ponniah, Paulraj. (2011). Data Warehousing: Fundamentals for IT Professionals.

New Delhi: Wiley India Pvt Ltd.

Prabhu, C. S. R. (2008). Data Warehousing: Concepts, Techniques, Products and

Applications. New Delhi: PHI Publications.

Inmon, W. H. (2005). Buidling the Data Warehouse. London: Wiley Publications.






First Semester

CO1B04TM - DBMS & Data Warehousing


Section A

Answer any 5 questions. Each question carries 2 marks (5 × 2=10)

Q1. What is primary key in a database?

Q2. What are the different properties of RDBMS?

Q3. What is data type in a database?

Q4. What is metadata?

Q5. What are real time transactions and batch transactions?

Q6. What is non-volatile property of Data warehouse?

Q7. What is real time Data Warehousing?

Q8. What are data quality factors?

Section B


Q1. Explain the key attributes of a Data Warehouse?

Q2. What is a normalization process in databases and why is it important to normalize tables?

Q3. What is the difference between data-mart and data-warehouse?

Q4. Explain the relevance of ETL tool in data warehousing?

Q5.How do the Internet, Intranet and Extranet apply to the data warehouse?

Q6. Why is it a good practice to load the dimensions table before the fact tables?

Q7. What is Data Visualization? Explain.

Q8. What are Fact Tables and Dimension Tables?? Explain.

Section C


Q1. Explain the architecture of Data Warehouse?

Q2. Explain the different types of fragmentations in Distributed Databases? What are the

disadvantages of distributed databases?

Q3. Why are ETL functions considered to be most challenging in a data warehouse

environment?

Q4. How is OLAP different from Data Mining?

Q5. What is OLAP? What are the different types of OLAP? Explain

Q6. Case Study:



You are selected to be a CIO of a Fortune 500 company. You have just made a presentation to

your CEO, proposing that the company implement a data warehouse. The CEO responds that

the company is already having several databases in different places and needs to know the

necessity of a data warehouse. The CEO also wants to implement Data mining for customer

relationship which would give the company effective ways for analysing customer behaviour

together with the power to make projections based on that customer information. The

company‘s activities also focus on the internet. The data mining also helps it to determine

seasonal buying habits. Data mining is helping the company also to study future trends, pattern

analysis and helps it not only retain existing customer base but also diversify the business in

new areas.

Question: (1) List down the key activities involved in the designing of a data warehouse.

(2) What is the recipe for a successful data warehouse?

(3) What makes data mining possible?

(4) What are the pitfalls of a data warehouse?




Name of the Course: Spreadsheet Modelling and Decision Analysis


Semester : 1

Aim of the Course: This course will cover all aspects of creating spreadsheet, performing

calculations, formatting, some very widely used formulas like SUMIF, LOOKUPS, and also

getting Excel to make decisions using the ‗IF‘ function, ―WHAT IF ANALYSIS‖ and

conditional formatting. It will enable the students to create, build and customise graphs,

develop advanced solutions on the worksheet and to assemble the proper Excel tools.

Students will gain an insight into data tables and using excel advanced look up features to

automate worksheets and analysis tools to forecast figures based on a range of scenarios and

use consolidation to bring together information.

The case study based training design is unique as it blends the features of the tool and usage

scenarios.

Course Overview and Context: Successful management requires the ability to recognize a

decision situation, understand its essential features, and make a smart choice; however in the

business world the stakes may be too high to learn by experience. Use decision models—

simplified representations of situations involving uncertainty and/or complex interactions to

help you gain the necessary skills to make the best decision. Discover several commonly used

modelling tools (Data Analysis using Pivot Table, Filters and What If Analysis) and learn the

art of modelling in a Microsoft Excel environment. Familiarity with Excel.

Syllabus Content

Unit 1 8 hours

Starting up with MS Excel

Basics of MS Excel: The Ribbon, The levels of Command organization Excel Options,

Customizing the Quick Access Tool Bar, The Excel Screen, Worksheets and Workbooks,

Saving a workbook. Manipulating Rows And Columns: Inserting rows and columns within a

worksheet, modifying column widths using 'drag and drop', Automatically resizing the column

width to fit contents.

Unit 2 8 hours

Manipulating Worksheets

Switching between worksheets, Copying or moving worksheets between workbooks,

Grouping Worksheets, Protecting Worksheets and Workbooks. Working With Data- Entering

Data: Entering numbers and text into cells, Default text and number alignment, AutoFill.

Selecting data, Copying, moving and deleting data, Formatting data: Built in and custom

formats, Currency symbol, Date Percentages, Alignment of content in cell range, Cell

orientation.



Unit 3 8 hours

Implementing Mathematical, Trigonometry, Statistical Functions Using Referencing

Arithmetic Functions, Absolute/Relative Cell Referencing, Mixed References, Formula

Auditing Tools, Formula Error Checking, Tracing Precedents/Dependents, Goto, Naming

individual or range cells, Deleting and amending named ranges, Using named cells/ranges in

formulae, Using SUMPRODUCT, SUMIF and SUMIFS, COUNTIF and COUNTIFS,

AVERAGEIF and AVERAGEIFS Functions, Using FLOOR/ CEILING/ MROUND/ MOD/

QUOTIENT Functions.

Unit 4 8 hours

Graphs and Validation Techniques

Different kinds of Graphs, Column, Pie, Bar, Area, Scatter, Drop Down Lists, Error Alerts,

Conditional Formatting.

Unit 5 8 hours

Date, Text, Logical Functions

Date Functions: DAYS360, TODAY, NOW, NETWORKDAYS, WORKDAY, Logical

Functions: IF, AND, OR and NESTED IF, TEXT Functions: MID, SEARCH, LEFT, RIGHT

FUNCTIONS, TRIM, CLEAN, UPPER, LOWER, DOLLAR FUNCTIONS, SUBSITUTE.

Unit 6 8 hours

Lookup Functions

Lookup Functions: BASIC VLOOKUP and HLOOKUP, IFERROR VLOOKUP, MULTIPLE

COLUMN VLOOKUP, INDEX and MATCH Functions, OFFSET/ INDIRECT functions.

Unit 7 8 hours

Data Analysis using Pivot Table, Filters and What If Analysis

Data Analysis using Pivot Tables: Using the Pivot Table Wizard, Changing the pivot table

layout, Formatting, Grouping items, Inserting calculated fields, Pivot Table Options, Display

and hide data in fields, Lay out reports directly on worksheet, Pivot Chart, Using Slicer in

pivot Tables, Applying and Using AutoFilter, Creating a Custom AutoFilter, Sorting On More

Than One Column, Using Advanced Filter, Using Formulas In Criteria.



Unit 8 4 hours

Advance Tools

What If Analysis. Scenario Manager, Goal Seek, Solver.


C1. Understand the Basics of MS Excel.

C2. Understand the Customization of the Quick Access Tool Bar, the Excel Screen,

Worksheets and Workbooks,

C3. Understand the technique of Manipulating Worksheets.

C4. Understand the process of Protecting Worksheets and Workbooks.

C5. Identify the steps for Alignment of content in cell range and Cell orientation.

C6. Understand the Implementation of Mathematical, Trigonometry, Statistical Functions

Using Referencing.

C7. Implementation of the sumproduct, sumif and sumifs, countif and countifs, averageif and

averageifs functions, using floor/ ceiling/ mround/ mod/ quotient functions.

C8. Sketch the Different kinds of Graphs such as Column, Pie, Bar, Area, Scatter

C9. Identify the importance of Conditional Formatting.

C10. Understand the Date, Text, Logical Functions.

C11.Understand the responsibility accounting & performance budgeting.

C12. Understand the use of Lookup Function.

C13. Analysis of data using Pivot Table, Filters and What If Analysis

C14. Perform the Lay out reports directly on worksheet.

C15. Application of Advance Tools through What If Analysis. Scenario Manager, Goal

Seek, Solver.



Learning Resources:

Textbook

Walkenbach, John. (2010). Excel 2010 Bible. New Delhi: Wiley India Pvt Ltd.

MacDonald, Mathew. (2010). Excel 2010: The Missing Manual. Sebastopol: O'reilly.

Ragsdale, Cliff. T. (2008). Spreadsheet Modelling and Decision Analysis. New York:

Thomson south – western publications.

Monahan, George E. (2000).Management Decision Making: Spread Sheet, Modelling,

Analysis. London: Cambridge University


Module * Part A Part B Part C 1 1 1 1 2 1 1 1 3 1 1 1 4 1 1 1 5 1 1 1 6 1 1 7 1 1 1 8 1 1


First Semester

CO1B05TM - Spreadsheet Modeling and Decision Analysis

Time: 3 hour Maximum Marks: 50

Section A

Answer any 5 questions. Each question carries 1 marks (5 × 1 = 5 marks)

Q1. Give one feature of Ribbon and QAT. Q2. For the below values in cells A1 and B1; Write a function which has two conditions- A1 > 10 and B1 > 5 and returns the value CORRECT or INCORRECT if both are met.

Q3. State whether the statements below are true or false.



a) A line chart shows the relationship of the changes in the data over a period of time

b) A Column chart shows the relationship of the parts to the whole Q4. What does a lookup function do?

Q5. What is the difference between Count and Countif Function? Q6. What is sharing with Track changes feature?

Q7. What is a Concatenate Function?

Q8. Mention the shortcut key combinations used for navigating an excel sheet

i) to the top of the Worksheet (cell A1)

ii) Copy and Paste special Value

Section B

Answer any 5 questions. Each question carries 3 marks (5 × 3= 15 marks)

Q1. What is the sequence of operating mathematical operation in Excel? Using the order of

operations what are the steps an excel would perform to solve the below equation = (1520+1420)*2+(15^3)-1250/2

Q2. What is cell reference in pretext to a formula and why is it important? Differentiate between

relative, absolute and mixed cell referencing using the formulae below.

SUM(H2:J2)

SUM($H3:$J3)

SUM(H$4:J$4)

SUM($H$4:$J$4)

Q3. What is “IF” function? What is the difference between “AND” and “Or” function in relation to IF

function? Write the four functions for the below data:

If the first cell is greater than 10, True Else False

If the second cell is greater than 5, True else False

If one of the conditions are met or both conditions are met, Correct Else Incorrect

Q4. What is Hlookup? Using the data below; what would be the Hlookup function in cell B2?



Q5. What is a workbook? In terms of the review ribbon, what can be done with a workbook?

Q6. Explain the following features in Excel:

Protect and Share workbook

New Comment

Text to columns

Q7. What is data filter? Write steps for auto filter of data

Q8. Explain Correlation concept using scatter plots.

Section C


QQ11.. WWhhaatt iiss tthhee uussee ooff ggooaall sseeeekk ffuunnccttiioonn iinn EExxcceell??

Q2. What is PowerPivot? State two functions of PowerPivot.

Q3. There are 250 observations with multiple responses to a survey question. We want to know the

specific responses to that question. What do we do get the unique records?

Give at least two uses of this technique.

Q4. Explain the below functions



QQ55.. WWrriittee ffoorrmmuullaass ffoorr tthhee ooppeerraattiioonnss ((aa ttoo cc)) bbaasseedd oonn tthhee sspprreeaaddsshheeeett ggiivveenn bbeellooww aalloonngg

wwiitthh tthhee rreelleevvaanntt cceellll aaddddrreessss::

A B C D E F G H

1 SNO Name Science Maths

Comput

ers

Total

Marks

Out of

Marks

Percenta

ge

earned

2 1 Swati 70 80 87 -- 300

3 2 Shruti 90 98 89 -- 300

4 3 Neelu 91 90 99 -- 300

5 4 Rosy 60 76 79 -- 300

6 5 Shreya 50 45 20 -- 300

7 # Fail --

8

Max

Score --

a. Total marks scored by each student

b. Percentage scored by each student

c. Number of students failed in each subject where (Marks < 30 is considered Fail)

d. Max score achieved in each subject

e. Does the cell reference change when you copy the formula below for Total marks scored?

QQ66.. BBaasseedd oonn tthhee sspprreeaaddsshheeeett ggiivveenn bbeellooww aalloonngg wwiitthh tthhee rreelleevvaanntt cceellll aaddddrreessss:: DDoo tthhee

ffoolllloowwiinngg

How do you calculate the count of observations? Write the function

Give a unique name "GI_name_id" by joining two text strings namely "ID" and "GI_name".

Maintain a space between the two. Write the function.



What function would you use if we want only the last two digits of the Area code?

What function would you use if we want the "Call procedure" variable to be coverted to a

dummy variable where Satisfied=1 and Not Satisfied=0?




Name of the Course: Statistics for Business


Semester - 1

Aim of the Course: The objective of this subject is to give students a conceptual introduction

to the field of statistics and its many applications. This is to enable the students to familiarise

themselves with descriptive statistics, probability and sampling distributions, hypothesis

testing, forecasting, nonparametric methods, decision analysis, time value of money, simple

and compound interest, discounting and compounding, present value and future value.

Course Overview and Context: This course teaches the core communication and quantitative

skills principle. In particular, the course teaches how to perform quantitative analysis. The

course also teaches the critical thinking principle. The course teaches how to use data,

combined with logic, to reach valid solutions to challenging problems. Also, the course

teaches students how to think critically about ambiguous situations. Finally, the idea that

underlies statistics is subtle and powerful. Hence, by teaching this idea, the course advances

students‘ intellectual depth.

Syllabus Content

Unit 1 9 hours

Descriptive Statistics

Introduction, summarizing Data, EDA, Crosstabulation, measures of location, measures of

variability, measures of distribution shape, measures of association, applications with Minitab

and Excel.

Unit 2 8 hours

Probability Distributions

Introduction, Bayes‘ Theorem, Discrete Probability Distributions, Continuous Probability

Distributions, Binomial Distribution, Poisson Distribution, Approximation of Binominal

Distribution to Normal Distribution.

Unit 3 7 hours

Sampling Distributions

Sampling and Sampling Distributions, Central Limit Theorem, Concept of Standard Error,

Confidence Intervals, Applications with Minitab and Excel.

Unit 4 12 hours

Hypothesis Testing



Hypothesis Testing and Decision Making, Statistical Inferences about means and proportions

and variances, Test of Goodness of Fit and Independence, Applications with Minitab and

Excel.

Unit 5 5 hours

Additional Topics

Index Numbers, Forecasting, Decision Analysis, Control Charts, Applications with Minitab

and Excel.

Unit 6 5 hours

Nonparametric Methods

Related samples, Wilcoxon Signed Rank, Mann-Whitney test, Friedman AVOVA test

Unit 7 9 hours

Financial Mathematics

Time Value of Money, Simple and Compound Interest, Discounting and Compounding,

Present Value and Future Value of single sum and annuity, Loan payments and Amortisation,

Applications with Excel.

Unit 8 5 hours

Correlation and Regression

Analyses of Relationship, Ordinary Least Square Regression, Standard error of estimate,

Adjusted R square and goodness of fit.


C1. Understand the Concept of Descriptive Statistics.

C2. Understand the summarization of Data using Cross tabulation.

C3. Understand the measures of variability, measures of distribution shape, measures of

association, applications with Minitab and Excel.

C4. Understand the concept of Probability Distribution & Bayes‘ Theorem.

C5. Identify the various distributions such as Continuous Probability Distributions, Binomial

Distribution, Poisson Distribution.

C6. Understand the process of Approximation of Binominal Distribution to Normal

Distribution.



C7. Recognise the process of Sampling and Sampling Distributions, and applicability of

Central Limit Theorem.

C8. Understand the Applications with Minitab and Excel.

C9. Identify the importance of Hypothesis Testing and Decision Making using Test of

Goodness of Fit and Independence, Applications with Minitab and Excel.

C10. Understand the Concepts of Forecasting, Decision Analysis & Control Charts.

C11. Understand the application of Nonparametric Methods in case of related samples.

C12. Understand the benefits of Time Value of Money.

C13. Determination of Loan payments and Amortisation using Excel.

C14. Identify the need of Correlation and Regression.

C15. Understand the Concept of Adjusted R square and goodness of fit.

Learning Resources:

Anderson, David R., Thomas A. Williams and Dennis J. Sweeney. (2012). Statistics for

Business and Economics. New Delhi: South Western.

Levin, Richard I. and David S. Rubin (1994). Statistics for Management. New Delhi:

Prentice Hall.

Waller, Derek. (2008). Statistics for Business. London: BH Publications.

Lee, Cheng. et al. (2013). Statistics for Business and Financial Economics. New York:

Heidelberg Dordrecht.





PGDM- Business Analytics – CBCSS Exam First Semester

CO1B06TM - Statistics for Business


Section – A


9. Define cross tabulation.

10. What is Baye‘s theorem?

11. What is a sample space?

12. What is discrete random variable?

13. Define sampling.

14. What is Central limit theorem?

15. What is goodness of fit?

16. Define F-statistic.

Section – B

Answer any 5 questions. Each question carries 3 marks (5 × 3 =

15)

9. What are the various methods of collecting primary data?

10. There are 4 boys and 2 girls in a room A and 5 boys and 3 girls in room B. A girl from

one of the two rooms laughed loudly. What is the probability that the girl who laughed

was from room B?

11. Find the binomial distribution if the mean is 12 and standard deviation is 2.

12. The morality rate for a certain disease is 7 in 1000. What is the probability for just 2

deaths on account of this disease in a group of 400? Given e-2.8

= 0.06.

13. Out of 300 households in a town 123 have T.V. sets. Find 95% confidence limits to the

true value of the proportion of households with T.V sets in a whole town?



14. If 10 coins are tossed 100 times, how many times would you expect 7 coins to fall head

upward?

15. Mr. Jack deposits Rs, 2000 on 1/1/2013 in Citi bank that gives 8% interest

compounded annually. How much would the deposit grow to after 6 years and 10

years?

16. Mr. Kevin deposited Rs 8000 in ABS bank. The bank is offering an interest rate of 8%

on savings account. Kevin is looking to get double the return for the amount he has

deposited. Compute the doubling period using rule of 72 and rule of 69.

Section – C

Answer any 3 questions. Each question carries 10 Marks (3 × 10 =

30)

17. Before an increase in excise duty on tea 400 people out of a sample of 500 persons

were found to be tea drinkers. After an increase in the duty, 400 persons were known

to be tea drinkers in a sample of 600 people. Do you think that there has been a

significant decrease in the consumption of tea after the increase in excise duty?

18. A sample of 900 members has a mean 3.4cms and standard deviation 2.61 cms. Can the

sample be regarded as one drawn from a population with mean 3.25 cms? Using level of

significance as 0.05, is the claim acceptable?

19. A machine produced 20 defective articles in a batch of 400. After overhauling, it

produced 10 defectives in a batch of 300. Has the machine improved?

20. Random samples are drawn from two populations and the following results were

obtained:

Sample

X

16 17 18 19 20 21 22 24 26 27

Sample

Y

19 22 23 25 26 28 29 30 31 32 35 36

Find the variance of two populations and test whether the two samples have same variance.



21. A farmer applied three types of fertilizers on 4 separate plots. The figure on yield per

acre are tabulated below:

Fertilizer Plots

Yield

Total A B C D

Nitrogen 6 4 8 6 24

Potash 7 6 6 9 28

Phosphates 8 5 10 9 32

Total 21 15 24 24

Find out if the plots are materially different in fertility, as also, if the three fertilizers make any

material difference in yields.

22. Determine the equation of a straight line which best fits the following data:

Year 2000 2001 2002 2003 2004

Sales (in Rs

‘000)

35 56 79 80 40

Compute the trend values for all the years from 2000 to 2004.




Name of the Course: Financial Markets and Instruments


Semester - 2 Aim of the Course: The objective of the course is to help the students to understand the

macro process and system of Indian Financial Markets at the broader level by exposing them

to concepts such as credit creation, productive ventures, money management by government

through RBI, money supply, liquidity, lending, borrowing and facilitation of credit on demand,

issuers and subscribers and generation of income for lenders on short term and long term basis.

The students would be exposed to operations as well as systems and procedures that are

commonly followed in the financial markets which by learning would help them in

understanding the financial markets better.

Course Overview and Context: The course gives a brief understanding of Indian financial system and its structure. It tells about the role of financial sectors in Indian Economy. The course also gives a detailed description of the structure of government securities market, money market and capital market. It talks about the role of government and RBI in the development of financial system. The course gives a detailed insight of the derivatives market in simpler language and also the importance of mutual funds as an investment vehicle. Syllabus Content Unit 1 11 hours Indian Financial System and Structure Financial Markets, Financial Assets, Financial Instruments and Financial Institutions, their role in the development of the financial system, role of government and RBI, money supply, mobilization and channelization, credit creation, utilizing mobilized funds into productive ventures, structure of G-sec market, structure of Money Market and Bond Market, instruments of lending and borrowing in the money market including participants, introduction to CBLO segment, role of Primary Dealers, institutions participation, retail participation, various short term and long term fund raising exercises by various entities on risk-free basis, understanding yield related technicalities. Unit 2 8 hours Primary Market and Public Issue Management: Fund raising practices in the 1980s and 1990s, SEBI regulations on fund raising from public (ICDR Regulations), role of Angel, VC and PE investors, VC and PE exits, public issue and its purpose, types of issues, role of merchant bankers, role of other intermediaries, book building process, bidding process, types of investors, basis of allotments, underwriting, listing formalities. Unit 3 9 hours



Secondary Markets Role of stock exchanges, role of stockbrokers, secondary market mechanism, type of

participants – traders and investors, index and its utility, risk management by way of

collecting margins, exchange settlement procedures, trading mechanism, pay-offs and

arriving at financial positions, touching upon technical analysis and fundamental

analysis.

Unit 4 8 hours Derivatives Market and Risk Management: Purpose of derivatives, concepts of futures, trading, hedging and arbitrage mechanism, margins and ledger credits, how to protect portfolios from devaluation, touching upon a few options strategies. Unit 5 9 hours Mutual Funds and other instruments Purpose of mutual funds, its structure, fund management basics, type of themes – debt, equity and hybrid themes, mutual fund themes, utility to investors, options available to invest, mutual fund investing v/s direct investing. Competencies of the course C1. Understand the meaning of Financial Markets.

C2. Explain the Structure of Financial System in India

C3. Familiarize with Important Financial Instruments available for investment in India.

C4. Discuss the capital market and money market and its financial instruments.

C5. Familiarize with Industrial securities market and derivatives market operation in India.

C6. Discuss the role role of Domestic Institutional Investors and Foreign Institutional

Investors in Indian financial market

C7. Elucidate RBI functions and organization and its regulatory role in the banking sectors.

C8. Understand the various trading mechanisms and pay-offs.

C9. Understanding the concept of technical and fundamental analysis.

C10. Understand the concept of derivatives.

C11. Understand various hedging and arbitrage mechanisms.

C12. Explain Venture capital role in the economic development of a country

C13. Discuss Stages and features of Venture Capital financing

C14. Understanding the concepts of mutual fund and its structure.

C15. Understanding various types of mutual funds and the purpose of investing in mutual

funds.



Learning Resources

Khan, M. Y. (2005). Financial Services. New Delhi: Tata McGraw Hill.

Bhole, L. M. (2004). Financial Institutions and Markets – Structure, Growth and

Innovations. New Delhi: McGraw Hill.

Parameswaran, Sunil. (2011). Fundamental of Financial Instruments. Singapore:

John Wiley and Sons Pte. Ltd.

Weiss, David M. (2009). Financial Instruments. New York: Portfolio Hardcover.

Babu, G. Ramesh. (2005). Financial Services in India. New Delhi: Concept

Publishing Company.



PGDM- Business Analytics

Second Semester

CO2B07TM - Subject: Financial Markets and Instruments


Section – A

Answer any 5 Questions .Each Question carries 2 Marks (5 × 2 = 10)

17. Define financial system.

18. What are the components of a financial system?

19. What are primary and secondary securities?

20. Define T-bills and call money market.

21. Define mutual funds.



22. Define venture capital.

23. What do you understand by Collaterized Borrowing and Lending Obligations (CBLO)?

24. What do you understand by Monetary Policy?

Section – B

Answer any 5 Questions .Each Question carries 4 Marks (5 × 4 = 20)

23. Describe the features of secondary market.

24. What are the various methods of raising capital in a primary market?

25. Describe domestic and foreign institutional investors.

26. Discuss the various monetary objectives of RBI.

27. What are forward and future contracts?

28. What are the various types of mutual funds?

29. What are the functions of a broker in secondary market?

30. What is the difference between technical and fundamental analysis?

Section – C


31. Discuss the short-term and long-term methods of raising finance for a company?

32. Describe the role of primary dealers in the government securities market?

33. Discuss the role of government and RBI in money market.

34. What are the functions of stock exchanges in secondary market?

35. Describe the various functions of merchant banks.

36. What are the features of venture capital and describe the stages in Venture Capital

Financing?




Name of the Course: Introduction to Entrepreneurship


Semester - 2

Aim of the Course: The Aim of this subject is to familiarise the students with the

fundamentals of entrepreneurship, business principles and ideas – with a focus on global

business environment; to help them understand the theories of Entrepreneurship,

Entrepreneurial Motivation, how to form a Business plan; to familiarise them with laws and

regulations that govern entrepreneurship.

Course Overview and Context: The Course intended to provide students with a solid

foundation in terms of the vital role played by entrepreneurs and entrepreneurship in the 21st

century global economy. In a sense this is the cornerstone course, which is complemented at

the end of your program with the capstone business plan course. During this semester we will

assess, explore, critique, and celebrate the phenomenon of entrepreneurship. Entrepreneurship

is approached as a way of thinking and acting, as an attitude and a behavior. Our emphasis is

on entrepreneurship as a manageable process that can be applied in virtually any

organizational setting. This is a course of many ideas and questions, and you will be

encouraged to develop and defend your own set of conclusions regarding each of these issues.

Syllabus Content

Unit 1 12 hours

Theories of Entrepreneurship

Who is an Entrepreneur?, Theories of Entrepreneurship, Economic Entrepreneurship Theories,

Psychological Entrepreneurship Theories, Personality Traits theory, Locus of Control, Need

for Achievement theory, Traits of Successful Entrepreneurs, Types of Entrepreneurs, Clarence

Danh of Classification, Arthur H. Cole Classification, Classification on the Basis of

Ownership, Classification Based on the Scale of the Enterprise.

Unit 2 8 hours

Entrepreneurial Motivation

Entrepreneurial Motivation, the Needs Framework, Entrepreneurship Development

Programmes (EDPs), The Kakinada Experiment, Objectives of Entrepreneurship Development

Programmes (EDPs), The Entrepreneurship Development Programme Model in India, Criteria

for the Evaluation of EDPs.



Unit 3 8 hours

Forming a Business Plan

Forming a Business, Business Structure, Creating a Business Plan, Market Size Analysis,

Funding a Business Plan, Regulations and Laws, Sustaining a Business, Customer Satisfaction,

Commerce Bank, Customer Service scenario in India, Managing cash flows, Employee Buy-in,

HCL, Government Incentives and Resources.

Unit 4 8 hours

Growing the Business

Growing the Business, Managing Creativity, Innovation and the Sense of Ownership in a

Growing Organization, Creativity and Innovation, Strategies for Growth, Organic and

Inorganic Growth, Growing the business across geographical borders.

Unit 5 9 hours

The Global Entrepreneur

Laws and regulations that govern entrepreneurship, Things to know and plan before setting up

a venture for the global market, Ideas to arrange for finances, Support system for

entrepreneurs, Identifying opportunities in the global market, Case studies.


C1. Understand the Theories of Entrepreneurship.

C2. Understand the Personality Traits theory.

C3. Understand the application of Traits of Successful Entrepreneurs.

C4. Understand the Classification Based on the Scale of the Enterprise.

C5. Identify the basis of Entrepreneurial Motivation.

C6. Understand the Needs Framework.

C7. Recognise the benefits of Entrepreneurship Development Programmes (EDPs),

C8. Understand the Objectives of Entrepreneurship Development Programmes (EDPs).

C9. Identify the importance of The Kakinada Experiment.



C10. Understand the Criteria for the Evaluation of EDPs.

C11. Understand the process of Creating a Business Plan.

C12. Understand the Management of cash flows.

C13. Understand the Analysis of Market Size.

C14. Identify the role of Laws and regulations that govern entrepreneurship.

C15. Understand the things to know and plan before setting up a venture for the global market.

Learning Resources:

Textbook

Hatten, Timothy S. (2011). Small Business Management: Entrepreneurship and Beyond,

Mason: Cengage Learning.

Bansal, Rashmi. (2012). Connect the Dots. New Delhi: Westland.

Bansal, Rashmi. (2012). Stay Hungry, Stay Foolish. New Delhi: Westland.

Hisrich, Robert. Michael Peters and Dean Shepherd. (2006). Entrepreneurship. New

Delhi: Tata McGraw-Hill Education.

Bohoney, Jason. (2011). The Entrepreneurship Toolkit: Successful Approaches to

Fostering Entrepreneurship. Washington: Weidemann Associates Inc.



PGDM- Business Analytics Second Semester

CO2B08TM - Subject: Introduction to Entrepreneurship Time: 03 hours Maximum marks: 75

Section – A


1. Define entrepreneurship.



2. What do you understand by sole proprietorship?

3. What is partnership?

4. Name any four their theories of entrepreneurship.

5. What is anthropology?

6. Define Fabian Entrepreneurs.

7. What is the classification of entrepreneurs based on Arthur H. Cole?

8. What do you understand by private entrepreneurs?

Section – B


9. What are the characteristics of entrepreneurs?

10. Describe the components of a business plan summary.

11. What do you understand by Market Size Analysis? Explain.

12. What are the possible ways to generate funding for business? Discuss.

13. What are the major aspects related to regulations and laws that an entrepreneur should be

aware of?

14. What are the advantages of entrepreneurship with respect to individual?

15. List down the problems related to EDP.

16. Explain Corporation.

Section – C


17. Discuss the theories of Entrepreneurship.

18. Explain the Kakinada experiment in detail.

19. Give an account of the following phases of Entrepreneurship Development Programme

(EDPs)

Pre-training Phase

Training Phase

Post-training or Follow-up Phase

20. Discuss the various types of Entrepreneurs.

21. Explain Entrepreneurship Development Program (EDP).

22. Explain how a business plan is created.




Name of the Course: Management Decision Science


Semester - 2

Aim of the Course: The aim is to equip students with the decision-making, managerial, and

technological skills necessary to succeed in a competitive business environment.

Course Overview and Context: Management Science, an approach to decision making based

on scientific method, makes extensive use of quantitative analysis. The purpose of this course

is to provide students with a sound conceptual understanding of the role that management

science plays in the decision-making process.

Syllabus Content

Unit 1 6 hours

Linear Programming

Introduction, Problem Formulation, Graphical solution procedure, Multiple Decision

Variables, Sensitivity Analysis, Applications with Minitab and Excel.

Unit 2 6 hours

Advanced Linear Programming

Applications in Marketing, Finance and Operations, Data Envelopment Analysis, Revenue

Management, Portfolio Models and Asset Allocation, Game Theory, Excel solution.

Unit 3 6 hours

Distribution and Network Models

Transportation Problem, Assignment Problem, Transhipment Problem, Shortest-Route and

Maximal Flow Problem, Excel solution.

Unit 4 6 hours

Integer and Goal Programming

Graphical and Computer solution for Integer Linear Program and Multicriteria Decision, AHP,

Excel solution.



Unit 5 6 hours

Project Scheduling, Queuing Models and Simulation

Project scheduling with known activity times, Project scheduling with uncertain activity times,

Time-Cost Trade-offs, Waiting Line Models, Simulation of Management Systems.

Unit 6 5 hours

Decision analysis basics. Use of decision trees, probabilities, methods for making decisions

under uncertainty

Unit 7 5 hours

Decision making under certainty, Risk and under uncertainty stations: Decision tree for

decision making, Breakeven analysis under uncertainty

Unit 8 5 hours

Statistical forecasting techniques, Auto correlation, Simultaneous equations models.


C1. Understand the Concept of Linear Programming.

C2. Understand the procedure to present Graphical solutions, using Multiple Decision

Variables, & Sensitivity Analysis.

C3. Understand the application of linear programming in the areas of Marketing, Finance and

Operations,

C4. Understand the concept of Probability Distribution & Bayes‘ Theorem.

C5. Identify the various distributions such as Continuous Probability Distributions, Binomial

Distribution, Poisson Distribution.

C6. Understand the process of Revenue Management, Portfolio Models and Asset Allocation.

C7. Recognise the process of Game Theory.

C8. Understand the Applications of Transportation Problem,



C9. Identify the importance of Shortest-Route and Maximal Flow leading to lower logistics

costs.

C10. Understand the Concepts of Integer Linear Program and Multi criteria Decision.

C11. Understand the application of Project Scheduling, Queuing Models and Simulation.

C12. Understand the application of decision trees for making decisions under uncertainty.

C13. Understand the Statistical forecasting techniques.

C14. Identify the need of Auto correlation.

C15. Understand the Concept of Simultaneous equations models.

Learning Resources:

Sweeney, Dennis J., et al. (2008). Introduction to Management Science. Mason: Cengage

Learning.

Vohra, N. D. (2006). Quantitative Techniques in Management. New Delhi: Tata McGraw

Hill.

Srivastava, U. K, G. V. Shenoy and S. C. Sharma. (2005). Quantitative Techniques for

Managerial Decisions. New Delhi: New Age International Ltd.

Wisniewski, Mik. (2009). Quantitative Methods for Decision Makers. London: Prentice-

hall publications.

Gupta, M. P. and R. B. Khanna. (2009). Quantitative Techniques for Decision Making.

New Delhi: PHI Learning Private Limited.



PGDM- Business Analytics

Second semester CO2B09TM - Subject: Management Decision Science

Time: 03 Hours Maximum Marks: 50 Section – A




Q1. Define the term Management Science.

Q2. Mention the steps of the decision-making process.

Q3. Define the Concept of a Critical Path.

Q4. What do you mean by Breakeven analysis?

Q5. What do you mean by Auto Correlation?

Q6. What are the Statistical forecasting techniques?

Q7. Define the concept of Probability Distribution

Q8. What is Game Theory?

Section – B


15)

Q9. Discuss the different roles played by the qualitative and quantitative approaches to

managerial decision making. Why is it important for a manager or decision maker to

have a good understanding of both of these approaches to decision making?

Q10. Suppose that a manager has a choice between the following two mathematical

models of a given situation: (a) a relatively simple model that is a reasonable

approximation of the real situation, and (b) a thorough and complex model that is the

most accurate mathematical representation of the real situation possible. Why might

the model described in part (a) be preferred by the manager?

Q11. Consider the following linear program:

Max 3A + 2B

s.t.

1A + 1B 10

3A + 1B 24

1A + 2B 16

A, B 0

a. Use the graphical solution procedure to find the optimal solution.

b. Assume that the objective function coefficient for A changes from 3 to 5. Does the optimal

solution change? Use the graphical solution procedure to find the new optimal solution.

c. Assume that the objective function coefficient for A remains 3, but the objective function

coefficient for B changes from 2 to 4. Does the optimal solution change? Use the graphical

solution procedure to find the new optimal solution.

d. The computer solution for the linear program in part (a) provides the following objective

coefficient range information:

Objective Variable Allowable Allowable

Coefficient Increase Decrease

A 3.00000 3.00000 1.00000

B 2.00000 1.00000 1.00000

Use this objective coefficient range information to answer parts (b) and (c).

Q12. Consider the following linear program. The value of the optimal solution is 27.

Suppose that the right-hand side for constraint 1 is increased from 10 to 11.

Max 3A + 2B

s.t.

1A + 1B 10

3A + 1B 24

1A + 2B 16



A, B 0

a. Use the graphical solution procedure to find the new optimal solution.

b. Use the solution to part (a) to determine the dual value for constraint 1.

c. The computer solution for the linear program in Problem 1 provides the following right-

hand-side range information:

RHS Allowable Allowable

Constraint Value Increase Decrease

1 10.00000 1.20000 2.00000

2 24.00000 6.00000 6.00000

3 16.00000 Infinite 3.00000

What does the right-hand-side range information for constraint 1 tell you about the dual

value for constraint 1?

d. The dual value for constraint 2 is 0.5. Using this dual value and the right-hand-side

range information in part (c), what conclusion can be drawn about the effect of changes to

the right-hand side of constraint 2?

Q13. The Westchester Chamber of Commerce periodically sponsors public service

seminars and programs. Currently, promotional plans are under way for this year‘s

program. Advertising alternatives include television, radio, and newspaper. Audience

estimates, costs, and maximum media usage limitations are as shown.

Constraint Television Radio Newspaper

Audience per advertisement 100,000 18,000 40,000

Cost per advertisement $2000 $300 $600

Maximum media usage 10 20 10

To ensure a balanced use of advertising media, radio advertisements must not exceed 50% of

the total number of advertisements authorized. In addition, television should account for at

least 10% of the total number of advertisements authorized.

a. If the promotional budget is limited to $18,200, how many commercial messages should be

run on each medium to maximize total audience contact? What is the allocation of the budget

among the three media, and what is the total audience reached?

b. By how much would audience contact increase if an extra $100 were allocated to the

promotional budget?

Q14. The following payoff table shows profit for a decision analysis problem with

two decision alternatives and three states of nature:

State of Nature

Decision Alternative s1 s2 s3

d1 250 100 25

d2 100 100 75

a. Construct a decision tree for this problem.

b. If the decision maker knows nothing about the probabilities of the three states of nature,

what is the recommended decision using the optimistic, conservative, and minimax regret

approaches?



Q15. A company imports goods at two ports: Philadelphia and New Orleans.

Shipments of one product are made to customers in Atlanta, Dallas, Columbus, and

Boston. For the next planning period, the supplies at each port, customer demands, and

shipping costs per case from each port to each customer are as follows:

Customers Port

Port Atlanta Dallas Columbus Boston Supply

Philadelphia 2 6 6 2 5000

New Orleans 1 2 5 7 3000

Demand 1400 3200 2000 1400

Develop a network representation of the distribution system (transportation problem).

Q16. Consider the following all-integer linear program:

Max 5x1 + 8x2

s.t.

6x1 + 5x2 30

9x1 + 4x2 36

1 x1 + 2 x2 10

x1, x2 ≥ 0 and integer

a. Graph the constraints for this problem. Use dots to indicate all feasible integer

solutions.

b. Find the optimal solution to the LP Relaxation. Round down to find a feasible

integer solution.

c. Find the optimal integer solution. Is it the same as the solution obtained in part (b)

by rounding down?

Section – C


30)

Q17. Consider the following linear program:

Min 8X + 12Y

s.t.

1X + 3Y 9

2X + 2Y 10

6X + 2Y 18

A, B 0

a. Use the graphical solution procedure to find the optimal solution.

b. Assume that the objective function coefficient for X changes from 8 to 6. Does the

optimal solution change? Use the graphical solution procedure to find the new optimal

solution.

c. Assume that the objective function coefficient for X remains 8, but the objective

function coefficient for Y changes from 12 to 6. Does the optimal solution change? Use the

graphical solution procedure to find the new optimal solution.



d. The computer solution for the linear program in part (a) provides the following

objective coefficient range information:

Objective Allowable Allowable

Variable Coefficient Increase Decrease

X 8.00000 4.00000 4.00000

Y 12.00000 12.00000 4.00000

How would this objective coefficient range information help you answer parts (b) and (c)

prior to re-solving the problem?

Q18. Consider the following linear program. The value of the optimal solution is 48.

Suppose that the right-hand side for constraint 1 is increased from 9 to 10.

Min 8X + 12Y

s.t.

1X + 3Y 9

2X + 2Y 10

6X + 2Y 18

A, B 0

a. Use the graphical solution procedure to find the new optimal solution.

b. Use the solution to part (a) to determine the dual value for constraint 1.

c. The computer solution for the linear program in Problem 3 provides the following right-

hand-side range information:

RHS Allowable Allowable

Constraint Value Increase Decrease

1 9.00000 2.00000 4.00000

2 10.00000 8.00000 1.00000

3 18.00000 4.00000 Infinite

What does the right-hand-side range information for constraint 1 tell you about the dual value

for constraint 1?

d. The dual value for constraint 2 is 3. Using this dual value and the right-hand-side range

information in part (c), what conclusion can be drawn about the effect of changes to the right-

hand side of constraint 2?

Q19. PhotoTech, Inc., a manufacturer of rechargeable batteries for digital cameras,

signed a contract with a digital photography company to produce three different

lithium-ion battery packs for a new line of digital cameras. The contract calls for the

following:

Battery Pack Production Quantity

PT-100 200,000

PT-200 100,000

PT-300 150,000

PhotoTech can manufacture the battery packs at manufacturing plants located in the

Philippines and Mexico. The unit cost of the battery packs differs at the two plants because of

differences in production equipment and wage rates. The unit costs for each battery pack at

each manufacturing plant are as follows:



Plant

Product Philippines Mexico

PT-100 $0.95 $0.98

PT-200 $0.98 $1.06

PT-300 $1.34 $1.15

The PT-100 and PT-200 battery packs are produced using similar production equipment

available at both plants. However, each plant has a limited capacity for the total number of PT-

100 and PT-200 battery packs produced. The combined PT-100 and PT-200 production

capacities are 175,000 units at the Philippines plant and 160,000 units at the Mexico plant.

The PT-300 production capacities are 75,000 units at the Philippines plant and 100,000 units

at the Mexico plant. The cost of shipping from the Philippines plant is $0.18 per unit, and the

cost of shipping from the Mexico plant is $0.10 per unit.

a. Develop a linear program that PhotoTech can use to determine how many units of each

battery pack to produce at each plant in order to minimize the total production and shipping

cost associated with the new contract.

b. Solve the linear program developed in part (a) to determine the optimal production plan.

c. Use sensitivity analysis to determine how much the production and/or shipping cost per unit

would have to change in order to produce additional units of the PT-100 in the Philippines

plant.

d. Use sensitivity analysis to determine how much the production and/or shipping cost per unit

would have to change in order to produce additional units of the PT-200 in the Mexico plant.

Q20. Data envelopment analysis can measure the relative efficiency of a group of

hospitals. The following data from a particular study involving seven teaching

hospitals include three input measures and four output measures:

a. Formulate a linear programming model so that data envelopment analysis can be used to

evaluate the performance of hospital D.

b. Solve the model.

c. Is hospital D relatively inefficient? What is the interpretation of the value of the

objective function?

Input Measures

Full-Time Supply Bed-Days

Equivalent Expense Available

Hospital Nonphysicians (1000s) (1000s)

A 310.0 134.60 116.00

B 278.5 114.30 106.80

C 165.6 131.30 65.52

D 250.0 316.00 94.40

E 206.4 151.20 102.10

F 384.0 217.00 153.70

G 530.1 770.80 215.00

Output Measures

Patient-Days Patient-Days

(65 or older) (under 65) Nurses Interns

Hospital (1000s) (1000s) Trained Trained



A 55.31 49.52 291 47

B 37.64 55.63 156 3

C 32.91 25.77 141 26

D 33.53 41.99 160 21

E 32.48 55.30 157 82

F 48.78 81.92 285 92

G 58.41 119.70 111 89

d. How many patient-days of each type are produced by the composite hospital?

e. Which hospitals would you recommend hospital D consider emulating to improve the

efficiency of its operation?

Q21. Consider the data presented below.

Input Measures

Full-Time Supply Bed-Days

Equivalent Expense Available

Hospital Nonphysicians (1000s) (1000s)

A 310.0 134.60 116.00

B 278.5 114.30 106.80

C 165.6 131.30 65.52

D 250.0 316.00 94.40

E 206.4 151.20 102.10

F 384.0 217.00 153.70

G 530.1 770.80 215.00

Output Measures

Patient-Days Patient-Days

(65 or older) (under 65) Nurses Interns

Hospital (1000s) (1000s) Trained Trained

A 55.31 49.52 291 47

B 37.64 55.63 156 3

C 32.91 25.77 141 26

D 33.53 41.99 160 21

E 32.48 55.30 157 82

F 48.78 81.92 285 92

G 58.41 119.70 111 89

a. Formulate a linear programming model that can be used to perform data envelopment

analysis for hospital E.

b. Solve the model.

c. Is hospital E relatively inefficient? What is the interpretation of the value of the objective

function?

d. Which hospitals are involved in making up the composite hospital? Can you make a general

statement about which hospitals will make up the composite unit associated with a unit that is

not inefficient?

Q22. Consider the following network representation of a transportation problem:



The supplies, demands, and transportation costs per unit are shown on the network.

a. Develop a linear programming model for this problem; be sure to define the variables in

your model.

b. Solve the linear program to determine the optimal solution.




Name of the Course: Business Finance


Semester - 2 Aim of the Course: The objective of this subject is to familiarise the students with meaning, nature and scope of finance, Valuation of Bonds and Shares, nature of investment decisions, Investment evaluation criteria, Operating and Financial Leverage, significance, theories of Capital Structure, types of Dividend Issues and factors influencing dividend decisions, Working Capital Management, Inventory Management. Course Overview and Context:The course gives an understanding of the concept of Time value of money. It tells about the various methods of valuing bonds and shares and the various types of risks involved in it. The cost gives a snapshot of capital budgeting and discuss about the concept of leverage. The course gives an overall understanding of various theories related to capital structure and dividend decision such M-M hypothesis, Walter’s model etc. Syllabus

Unit 1 8 hours

Overview

Financial Management: Meaning, nature and scope of finance, Time Value of Money, Valuation of Bonds and Shares, Risk and Return, Cost of Capital. Working Capital Management, Inventory Management, Cash Management, Receivables Management.

Unit 2 10 hours

Investment Decision

Capital Budgeting: Nature of investment decisions, Investment evaluation criteria -Net Present Value, Internal Rate of Return, Profitability Index, Payback Period, Discounted Payback Period, Accounting Rate of Return, Capital rationing, Risk analysis in capital budgeting, Statistical techniques, Probability, Expected NPV, Standard deviation and variance and coefficient of Variation, Sensitivity analysis, Scenario Analysis, Simulation Analysis.

Unit 3 10 hours

Financing Decision

Operating and Financial Leverage: Measurement of leverages, Effects of operating and financial leverage on profit, Analysing alternate financial plans, Combined financial and operating leverage, Indifference point, Financial Break Even point.



Capital Structure: Concept, importance and significance, theories of Capital Structure, Net Operating Income Approach, Net Income Approach, Traditional Approach and M.M. Hypotheses – without taxes and with taxes, determining capital structure in practice.

Unit 4 9 hours

Capital Structure and Dividend Decision

Dividend Policies: Concept, types of Dividend issues and factors influencing dividend decisions, Dividend Theories: Irrelevance theory, M-M hypothesis, Relevance Theories: Walter’s model. Gordon’s model, dividend policy in practice, type of dividend policies, stability in dividend policy, corporate dividend behaviour.

Unit 5 8 hours

Exchange Rate Mechanism

Five key relationships, Purchasing Power Parity, Interest rate Parity, Fisher Effect, International Fisher Effect & Forward rate, Currency Arbitrage. Competencies of the course C1. Understand the concept of time value of money.

C2. Explain the valuation of bonds and shares

C3. Familiarize with concepts such as Working Capital Management, Inventory

Management, Cash Management.

C4. Understanding the concept of Capital Budgeting.

C5. Familiarize terms like internal rate of return, profitability index, payback period,

Discounted payback period.

C6. Understanding the risk and return analsysis.

C7. Discuss standard deviation, variance and coefficient of Variation.

C8. Familiarize with various analysis such as Sensitivity analysis, Scenario Analysis,

Simulation Analysis. .

C9. Understanding the concept of leverage and types of leverage.

C10. Understand the importance and significance, theories of Capital Structure, Net

Operating Income Approach, Net Income Approach.

C11. Familiarize with capital structure and dividend decision.

C12. Discuss Walter’s model. Gordon’s model, dividend policy in practice, type of

dividend policies

C13. Understand the concept of purchasing power parity



C14. Understanding the concepts of fisher effect.

C15. Understanding arbitrage mechanism in currency market.

Learning Resources:

Pandey, I. M. (2010). Financial Management. New Delhi: Vikas Publishers..

Chandra, Prasanna. (2008). Financial Management. New Delhi: Tata McGraw-Hill

Education.

Brealey, Richard A. et al. (2007). Principles of Corporate Finance. New Delhi: Tata

McGraw Hill.

Horne, Van and Wachowitz. (2009). Fundamentals of Financial Management. New

Delhi: Phi Learning.

Ross, Wetfield and Jaffer. (2008). Corporate Finance. New Delhi: Tata McGraw-Hill.

Madhu Vij. ( 2006) . International Financial Management. New Delhi : Excel Books

India.



PGDM- Business Analytics – CBCSS Exam Second Semester

CO2B10TM - Subject: Business Finance


Section – A


1. Define systematic and unsystematic risk.

2. What is standard deviation?

3. Define internal rate of return.



4. What are the characteristics of a bond?

5. Define leverage.

6. What is meant by NPV?

7. Define breakeven point.

8. Define coefficient of variance.

Section – B


20)

9. Mr. Jack deposits Rs, 2000 on 1/1/2013 in Citi bank that gives 8% interest

compounded annually. How much would the deposit grow to after 6 years and 10

years?

10. Describe the thumb rule for doubling period?

11. Compute the expected return

Return Probability of Return

4% 0.1

8% 0.2

12% 0.4

16% 0.2

20% 0.1

12. Explain the relationship between risk and return.

13. Company ABC is undertaking a project at a cost of $60 million which is expected to

generate future net cash flows with a present value of $75 million. Calculate the

profitability index.

14. The coupon rate on the bond is 7.50%, and the market interest rate is 7.75%. Compute

the price of the bond.

15. Define operating and financial leverage.

16. Explain purchasing power parity.



Section – C


45)

17. Explain Sensitivity, Scenario and Simulation analysis?

18. What are the majors steps involved in Capital Budgeting Process?

19. Suppose that your college roommate has approached you with an opportunity to lend

Rs 25,000 to her fledgling home healthcare business. The business, called Home

Health Care, Inc., plans to offer home infusion therapy and monitored in-the-home

healthcare services to surgery patients in the Bangalore. Funds would be used to lease

a delivery vehicle, purchase supplies, and provide working capital. Terms of the

proposal are that you would receive Rs 5,000 at the end of each year in interest with

the full Rs 25,000 to be repaid at the end of a ten-year period. Assuming a 10%

required rate of return, calculate the present value of cash flows and the net present

value of the proposed investment.

20. Discuss the M.M Hypothesis theory and the Walter Model?

21. Describe Fisher Effect.

22. Discuss Walter‘s Model and Gordon‘s Model.




Name of the Course: Introduction to Business Analytics Duration: One Semester

Semester - 2 Aim of the Course: The objective of this subject is to provide foundational knowledge associated with the domain of business analytics which will be useful in work environment. The course also aims at making the students aware of the applications of business intelligence. Course Overview and Context: The course familiarizes the students with all concepts of

business intelligence including what problems the technology of Data Warehouse (DW)

/Business Intelligence (BI) /Advanced Analytics (AA) solve for businesses and when an

organisation is ready for DW/BI/AA.

Syllabus Content

Unit 1

IT and Business Analytics 12 hours

Business View of Information Technology Applications, Business Enterprise

Organization, Its Functions, and Core Business Processes, Baldrige Business Excellence

Framework (Optional Reading) , Key Purpose of Using IT in Business, The Connected

World: Characteristics of Internet-ready IT Applications, Enterprise Applications

(ERP/CRM, etc.) and Bespoke IT Applications, Information Users and Their

Requirements.

Unit 2

Digital Data, OLTP and OLAP 12 hours

Types of Digital Data, Getting to Know Structured Data, Getting to Know Unstructured

Data, Getting to Know Semi-Structured Data, Difference Between Semi-Structured and

Structured Data. Introduction to OLTP and OLAP, OLTP (On-Line Transaction

Processing), OLAP (On-Line Analytical Processing), Different OLAP Architectures, OLTP

and OLAP, Data Models for OLTP and OLAP, Role of OLAP Tools in the BI Architecture,



Should OLAP be Performed Directly on Operational Databases?, A Peek into the OLAP

Operations on Multidimensional Data, Leveraging ERP Data Using Analytics.

Unit 3

Business Intelligence 12 hours

Getting Started with Business Intelligence, Using Analytical Information for Decision

Support, Information Sources Before Dawn of BI?, Business Intelligence (BI) Defined,

Evolution of BI and Role of DSS, EIS, MIS, and Digital Dashboards, Need for BI at Virtually

all Levels, BI for Past, Present, and Future, The BI Value Chain, Introduction to Business

Analytics.BI Definitions and Concepts, BI Component Framework, Who is BI for?, BI

Users, Business Intelligence Applications, BI roles and Responsibilities, popular BI tools.

Unit 4 Data Integration and Modeling 12 hours

Basics of Data Integration, Need for Data Warehouse, Definition of Data Warehouse,

What is a Data Man?, What is Then an ODS?, Ralph Kimball's Approach vs. Who Inmon's

Approach, Goals of a Data Warehouse, What Constitutes a Data Warehouse?, What is

Data Integration?, Data Integration Technologies, Data Quality, Data Profiling.

Multidimensional Data Modeling, Types of Data Model, Data Modeling Techniques, Fact

Table, Dimension Table, Typical Dimensional Models, Dimensional Modeling Life Cycle.

Unit 5

Performance Management and Enterprise Reporting 12 hours

Understanding Measures and Performance Measurement System Terminology, Navigating a Business Enterprise, Role of Metrics, and Metrics Supply Chain "Fact based Decision Making" and KPIS , KPI Usage in Companies, Where Do Business Metrics and KPIS Come From, Connecting the Dots: Measures to Business Decisions. Basics of Enterprise Reporting, Reporting Perspectives Common to All Levels of Enterprise, Report Standardization and Presentation Practices, Enterprise Reporting Characteristics in OLAP World, Balanced Scorecard, Dashboards, How Do You Create Dashboards?, Scorecards vs. Dashboards, The Buzz Behind Analysis.

Competencies of the course C1. Understand the business view of Information Technology Applications.

C2. Understand the key purpose of using IT in business.



C3. Understand the characteristics of Internet-ready IT Applications, Enterprise

Applications (ERP/CRM, etc.) and Bespoke IT Applications.

C4. Understand structured data, unstructured data and semi- structured data

C5. Understand the Role of OLAP Tools in the BI Architecture.

C6. Understand the usage of Analytical Information for Decision Support.

C7. Distinguish between Ralph Kimball's and Who Inmon's Approach

C8. Understand the meaning of data integration and data integration technologies.

C9. Identify the Enterprise Reporting characteristics in OLAP World.

C10. Understand the basics of Enterprise Reporting.

C11. Understand the various applications of Business Intelligence.

C12. Understand Balanced Scorecard and Dashboards.

C13. Understand the types of Data Model and Data Modeling Techniques.

C14. Understand the role of DSS, EIS, MIS, and Digital Dashboards.

C15. Understand the Dimensional Modeling Life Cycle.

Learning Resources

Prasad, R. N., and Acharya, Seema. (2011). Fundamentals of Business Analytics, 1st

Edition. New Delhi: Wiley India.

Shmueli, Galit, Patel, Nitin R. and Bruce, Peter C. (2011). Data Mining for Business

Intelligence. New Delhi: Wiley-India.

Kimball, Ralph and Ross, Margy. (2011). Practical Tools for Data Warehousing and

Business Intelligence, 2nd Edition. New Delhi: Wiley-India.

Biere, Mike. (2003). Business Intelligence for the Enterprise, First Edition. IBM

Press.





PGDM- Business Analytics – CBCSS Exam Second semester

CO2B11TM - Subject: Introduction to Business Analytics



Q9. Define the term Business Analytics.

Q10. Is it important to analyze unstructured data?

Q11. Mention two ways in which we can extract information from

unstructured data?

Q12. State three ways to store unstructured data?

Q13. What are the sources of structured data?

Q14. Define the concept of Semi-structured data.

Q15. What are two advantages of ROLAP?

Q16. What are various ETL tools available in the market?

Section – B


20)

Q17. Explain master data management.

Q18. Discuss the two best practices for managing the growth of unstructured data.

Q19. Explain XML with the help of an example.

Q20. Construct an ER diagram to illustrate the working of a bank. Make all the

necessary assumptions.

Q21. What are the various approaches to data integration? Explain.

Q22. Mention a few Business Intelligence tools in each of the following categories:

a. Databases that can support data warehouse.

b. Reporting

c. Business data analytics

Q23. State the difference between data warehouse and Data marts.

Q24. What do you mean by cross-sell and up-sell? Explain with examples.

Section – C


45)

Q25. Explain the following:

a. OLTP

b. OLAP

c. ERP

d. ROLAP

e. MOLAP

f. ODS

Q26. Business Intelligence? Why is it required by businesses? Explain how Business

Intelligence contributes to providing visibility into the enterprise performance.

Q27. Give a few examples of strategic, tactical and operational decisions.



Q28. Compare and contrast the various types of slow changing dimensions. Use an

example to better explain your answer.

Q29. Explain the following terms with an example each:

a. Customer analytics

b. Productivity analysis

c. Supply chain analysis

Q30. What are dashboards? How do you create dashboards?




Name of the Course: SPSS for Research Methodology


Semester - 2

Aim of the Course: The Aim is to make the students familiar with the concepts of Research

Methods starting from the variable types, types of research, scale of measurement to actual

application with SPSS. The subject will equip the students with the skills of running

appropriate SPSS commands and with that of decoding SPSS output for hypothesis testing and

for Correlation and Regression Analysis.

Course Overview and Context: The Course intended to provide students insight into SPSS

software as tool to make statistical decisions easy. This will enable the participants to have

access to tools which results into making more accurate decisions by converting data into facts

and figures.

Syllabus Content

Unit 1 8 hours

Introduction to SPSS: SPSS windows, Creation and saving of SPSS files, Variable view, Data

view, Split and select command.

Unit 2 8 hours

Research Types: Exploratory and Experimental Research, Qualitative and Quantitative

Research, Selection of Scale, Comparative and Non Comparative Scaling Techniques, Likert

Scale, Mathematically Derived Scales, Choosing a Scaling Technique, Randomness. Run test

for randomness.

Unit 3 8 hours

Questionnaire Design Process, Quality of a good questionnaire, type of interviewing method,

individual question content, choosing question structure, choosing question wording, Closed

and Open question, determining the order of question.

Unit 4 8 hours

Descriptive Statistics: Univariate descriptive analysis: Types of scales, Nominal, Ordinal,

Interval and Ratio Scales. Categorical and Continuous Data. Bar Charts, Pie Charts, Box Plots,

Stem and Leaf Diagram, Histogram, Mean Plot. Normalcy tests: Shapiro-Wilk and

Kolmogorov Smirnov Test of Normality.

Unit 5 7 hours



Normality assessment through Histogram, Probability Plot, Q-Q Plot. Outlier‘s assessment.

SPSS application.

Unit 6 7 hours

Hypothesis Testing I: Central Limit Theorem, Types I and Type II Error, Level of

Significance and Confidence Interval. Null and Alternate Hypotheses, Parametric and Non-

parametric tsets. One Sample T-test, Paired Sample T-test, Independent Sample T test. SPSS

application.

Unit 7 7 hours

Hypothesis Testing II: One way ANOVA, Chi-square test, Related Sample Wilcoxon Signed

Rank Test, Mann-Whitney Test, Kruskal-Wallis and Friedman ANOVA test. SPSS application.

Unit 8 7 hours

Correlation and Simple Linear Regression Analysis: Relationship through scatter plots,

Coefficient of correlation, Assumptions of Linear Regression, Standard Error of estimate,

Adjusted R Square, Goodness of fit, Residual Analysis. Durbin-Watson statistics and

Multicollinearity.


C1. Understand the basic uses of SPSS tool.

C2. Understand the use of Variable view & Data view.

C3. Application of the Split and select command.

C4. Understand the Exploratory and Experimental Research.

C5. Identify the various Research Types.

C6. Understand the Need of correct Selection of Scale.

C7. Recognise the benefits of Run test for randomness.

C8. Understand the Questionnaire Design Process.

C9. Identify the importance of The Quality of a good questionnaire.

C10. Understand the Criteria for the distinction of Categorical and Continuous Data.



C11. Understand the Test of Normality.

C12. Understand the Hypothesis Testing.

C13. Understand the Assumptions of Linear Regression.

C14. Identify the role of Residual Analysis.

C15. Understand the Durbin-Watson statistics and Multi collinearity.

Learning Resources:

Bajpai, N. (2009). Business Statistics. New Delhi: Pearson.

Nargundkar, R. (2003). Marketing Research. New Delhi: Tata McGrawHill.

Malhotra, N.(2008). Marketing Research. New Delhi: Pearson.

Kothari, C. R. (2004). Research Methodology Methods and Techniques. New Delhi: New

age International Publishers.

Chaudhary, C. M. (1991). Research Methodology. Jaipur: RBSA Publishers.



PGDM- Business Analytics – CBCSS Exam Second semester

CO2B12TM - Subject: SPSS for Research Methodology



Q31. How do you define a research problem?

Q32. Define the term Alternate Hypotheses.

Q33. Distinguish between Type I Error and Type II Error.

Q34. Define Likert Scale.

Q35. What is histogram?

Q36. What is meant by Quantitative Research?



Q37. For the following pairs of variables, indicate which one is independent and

which is dependent according to you?

a. Sales and number of salespeople

b. Product quality and sales

c. Service quality and satisfaction

d. Age and income

Q38. What should be the scale of the dependent variable in an ANOVA? What

should be the scale of the independent variables in an ANOVA?

Section – B


15)

Q39. What are the benefits of using SPSS for analysis?

Q40. Distinguish between correlation and regression

Ten students got the following percentage of marks in the course principles of Economics and

Statistics.

Roll No. : 1 2 3 4 5 6 7 8 9 10

Marks in Economics: 78 36 98 25 75 82 90 62 65 39

Marks in Statistics : 84 51 91 60 68 62 86 58 53 47

Calculate the coefficient of Correlation.

Q41. Differentiate between Exploratory and Experimental Research.

Q42. Find out the range and its coefficient for the following series:

Q43. Calculate correlation coefficient by appropriate method

X 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Y 30,000 50,000 60,000 80,000 1,00,000 1,10,000 1,30,000

Q44. Given below is the bivariate data

X 1 5 3 2 1 1 7 3

Y 6 1 0 0 1 2 1 5

1) Fit a regression line of Y on X and hence predict Y when x =10

2) Fit a regression line of X on Y hence predict X when Y=2.5

3) Calculate Karl Pearson‘s correlation coefficient.

Q45. Explain the concept of dependent and independent variables.

Q46. Describe how to interpret the final cluster centres for all the variables in the

data.



Section – C


Q47. Explain the following types of scales:

g. Ordinal Scale

h. Nominal Scale

i. Interval Scale

j. Ratio Scale

Q48. Explain different types of charts and graphs.

Q49. A company believes that the number of salespersons employed is a good

predictor of sales. The following table exhibits sales (in thousand rupees) and the

number of salespersons employed for different years.

Develop a simple regression model to predict sales bases on the number of

salespersons employed.

Q50. For the Mumbai city, the mean daily mobile bill is 32.79 Rs. A sample of 50

households of ‗south Mumbai‘ showed a sample mean of 30.63 Rs. Use a population

standard deviation of σ = 5.60 Rs.

a. Formulate hypotheses for a test to determine whether the sample data support the

conclusion that the mean daily mobile bill in the southern Mumbai is less than the

overall Mumbai city mean of 32.79.

b. Compute the value of test statistic

c. What is the p-value

d. At α= 0.01, what is your conclusion for accepting or rejecting the null hypothesis.

Q51. What is the difference between parametric tests and nonparametric tests?

Discuss in the light of how these tests are used in marketing research. Explain the

major advantages of non-parametric tests over parametric tests?

Q52. A company is concerned about its workers devoting more time than necessary

to paper work. The company‘s researcher has taken a random sample of 8‘ employees

from four major departments: production, housekeeping, HRD, and marketing to test

this. The researcher has collected data on weekly hours spent on paper work by the

employees as presented in the below table. Use the Kruskal-Wallis test to determine

whether there is a significant difference in the weekly hours spent by the employees of

the four departments on completing paper work.

Weekly hours spent on completing paper work by the employees of four different departments.



Production Housekeeping HRD Marketing

20 25 30 42

22 26 32 41

21 25 33 40

23 27 31 41

24 26 32 43

22 25 34 42

21 25 35 40




Name of the Course: Consumer Behaviour and CRM


Semester - 3 Aim of the Course: The aim of this course is to make students understand the concept of

consumer behaviour and also help in understanding the consumer buying decision.

Course Overview and Context:

The course gives an understanding of the consumer behaviour and the different models use in

determining consumer behaviour. The course talks about the decision making process and also

list down the factors that influence buying behaviour. It also tells about the Significance of

Customer Loyalty for Marketers. It gives a complete insight of the Customer Loyalty in Retail

Industry, Banking, and Insurance Industry.

Syllabus

Unit 1 10 hours

Overview of Consumer Behaviour

Understanding Consumer Behaviour- Meaning and Concept of Consumer and Customer,

Consumer Learning, Different Models in Consumer Behaviour, Consumer Decision

making process-Concept of Consumer Decision; Levels of Consumer Decision Making;

Consumer Decision Making Model, Changing Indian Consumer Behaviour-Drivers of

Change; Changing Consumer Trends; Rural Consumer Behaviour; New Consumption

Patterns, Organizational Buying Behaviour.

Unit 2 10 hours

Factors Influencing Consumer Buying Decision

Influence of Culture on Consumer Behaviour-Concept of Culture; The measurement of

Culture; Indian Core Values; Cultural aspects of emerging markets, Values, Lifestyles, and

Psychographics- Impact of Values, Lifestyles and Psychographics on buying behaviour;

Demographics, Lifestyles and Psychographics; Values and Value Systems, Group

Influence on Consumption- Role of reference groups; Effect of reference groups on

consumer decision making; Celebrity endorsements.

Unit 3 10 hours



Customer Loyalty Comprehension

Meaning and definition of customer loyalty, Significance of Customer Loyalty, Customer

Loyalty Ladder, Loyalty Principles, Benefits of Customer Loyalty, Customer Loyalty and

its relationship with customer satisfaction, Customer retention and Brand Loyalty,

Factors affecting customer loyalty formation, Rai Srivastava model of customer loyalty

formation, Drivers of Customer Loyalty.

Unit 4 9 hours

Customer Loyalty Outcomes

Characteristic Features of Behavioural Loyalty, Attitudinal Loyalty and Cognitive Loyalty,

Role of Customer Loyalty outcomes in business decisions, Significance of Customer

Loyalty for Marketers, Relationship Influencers of Customer Loyalty including factors

mediating customer loyalty relationship with other relationship influencers, Customer

Affinity, Customer Engagement.

Unit 5 6 hours

Customer Loyalty Measurement and Application

Measuring Customer Loyalty, Customer Loyalty measurement models and scales,

Influence of Service Quality on Customer Loyalty, Customer Loyalty in Retail Industry,

Customer Loyalty in Banking and Insurance Industry, Customer Loyalty Application in

Aviation Industry.

Competencies of the course C1. Understand the meaning and concept of consumer.

C2. Explain the models associated with consumer behaviour.

C3. Understand consumer decision making process and the changing consumer trends.

C4. Understanding the rural consumer and organizational buying behaviour.

C5. Describe the cultural aspects of emerging markets, values and lifestyles.

C6. Explain the role of reference groups on consumer decision making.

C7. Discuss the role of celebrity endorsements.

C8. Explain the meaning of customer loyalty and its significance.

C9. Discuss the benefits of customer loyalty and the concept of brand loyalty.

C10. Discuss Significance of Customer Loyalty for Marketers.

C11. Discuss Customer Loyalty in Retail Industry, Banking and Insurance Industry.

C12. Discuss Customer Loyalty Application in Aviation Industry



C13. Explain Rai Srivastava model of customer loyalty formation.

C14. Explain Organizational buying behaviour.

Learning Resources:

Hawkins, Best, Coney: Consumer Behaviour Building Marketing Strategy, Tata

McGraw Hill

H.Peer Mohammed: Customer Relationship Management, Vikas.

Panwar, J.S. Beyond Consumer Marketing, Sage Response Books, New Delhi.

Mukesh Chaturvedi and Abhinav Chaturvedi: Customer Relationship Management

An Indian Perspective. Excel Books.

Deon – Buyer Behaviour, Oxford University Press.






Name of the Course: Economic Analysis for Business Decisions


Semester : 3 Aim of the Course: The objective of this subject is to familiarise the students with the basic principles of economics, tools and techniques of Economics and application of the same in the competitive business world. The students will understand the determinants of demand for basic goods, production decisions under various time periods, market structure.

Course Overview and Context

The course gives an understanding of consumer‘s economic activities. It describes the concept

of utility function and the Relationship between consumers Income and spending. The course also helps in understanding the law of demand, law of supply. The course gives a basic understanding of production function, and the cost involved in decision making. The course also talks about the macroeconomic variables involved in business decision.

Syllabus

Unit 1 10 hours Basic Concepts of Economics Economic problems, Flow of economic activities, understanding consumer’s economic behaviour (Utility, Satisfaction, indifference behaviour), Relationship between consumers Income and spending, managerial economics- a way of thinking about business.

Unit 2 10 hours Managing Demand and Supply Law of demand, Understanding the determinants of demand for basic goods, Household durables, Luxuries, Exceptions, Constructing Demand equation, Demand elasticity. Law of Supply, supply determinants, supply equation, concept of Equilibrium. Unit 3 10 hours Production Costs and Business Decision Production function, production decisions under various time periods, scale of production and managerial decision. Types of costs and its significance in decision making, Cost related decisions under various time periods.

Unit 4 8 hours Market Structure Firm under competitive conditions as perfect and imperfect, market characteristics, price and output determination.



Unit 5 7 hours Macro Economics in Business Decision Government and market, National Income computation, Business cycle, inflation, Macroeconomic Policies. Competencies of the course C1. Understand the meaning and concept of economics.

C2. Explain utility function.

C3. Discuss the concept of managerial economics.

C4. Understanding the Relationship between consumers Income and spending.

C5. Understanding the concept of law of demand.

C6. Understanding the determinants of demand for basic goods, Household durables,

Luxuries, Exceptions, Constructing Demand equation, Demand elasticity.

C7. Discuss the law of supply and the concept of equilibrium.

C8. Explain the meaning production function and the production decisions under various

time periods.

C9. Discuss types of costs and its significance in decision making.

C10. Understand perfect and imperfect, market characteristics, price and output

determination.

C11. Discuss the concept of macro economics

C12. Explain the computation of National Income

C13. Explain inflation and Macroeconomic Policies.

Learning Resources:

Mcguigan, James R. (2010). Managerial Economics: Applications, Strategy and

Tactics. Mason: Cengage Learning.

Dean, Joel. (2008). Managerial Economics. New Delhi: PHI Learning Pvt Ltd.

Spencer, Milton H. (1973). Managerial Economics. Toronto: Irwin.

Shim Jae K. (2008). Economic Analysis for Business and Strategic Decisions.

Singapore: Global Professional Publishing.



Subject: Economic Analysis for Business Decisions Blue Print - Question Paper



Name of the Course: Financial Modelling


Semester - 3

Aim of the Course: The objective of course is make students learn how to design and

develop financial models for solving financial questions. The course would provide practical

hands on with Excel and how Excel is used in solving various financial concepts such as

present and future value, regression analysis and portfolio optimization.

Course Overview and Context:

The course gives an understanding of excel and VBA. It helps to familiarise with a number of

financial modelling methods, including present and future value, matrix calculations,

optimisation, regression analysis, simulation and the event study methodology.

Syllabus

Unit 1 9 hours

Introduction

Understanding Finance Functions present in Excel: Annuity Functions, Investment analysis

functions, Bond Functions, Depreciation Functions. Making Decisions: Examples of creating

Decision making formulas (Income tax calculator), Looking up values and conditional

formatting.

Analysing databases using Sort, Filter and Pivot Tables.

Case Exercises/ Practical Assignments/Activities.



Unit 2 9 hours

Sensitivity Analysis using Excel

Scenario Manager, Other Sensitivity Analysis Features, Goal Seek, Data Tables


Unit 3 9 hours

Simulation using Excel

Different Statistical Distributions used in Simulation.

Generating Random Numbers that follow a particular distribution.

Building Models in Finance using

Simulation.


Unit 4 9 hours

Financial Models Part-1

Preparing common size statements directly from Trial Balance , Forecasting Financial

Statements using Excel, Analysing Financial Statements by using Spreadsheet Model.


Unit 5 9 hours

Financial Models Part-2

Time Value of Money: Loan Ammortization Table, Financial Planning & Investments:

Retirement Planner, Portfolio Structuring, Bond Pricing, Option and Option Portfolio: Option

Payoffs at Expiration.


Competencies of the course

C1. Understand annuity function, investment analysis function.

C2. Understand depreciation function using excel and conditional formatting.

C3. Analysing databases using Sort, Filter and Pivot Tables.

C4. Understanding sensitivity analysis such as goal seek.

C5. Familiarize with different Statistical Distributions used in Simulation.

C6. Discuss building models in Finance using simulation.



C7. Understanding the preparation of common size statements directly from Trial Balance.

C8. Analysing Financial Statements by using Spreadsheet Model.

C9. Understanding Loan Ammortization Table.

C10. Understanding option pricing and option portfolio.

Learning Resources:

Textbook

Benninga, S. Financial Modelling, 3rd edition, Massachusetts: MIT Press, 2008

Jonathan Swan, Practical Financial Modelling a guide to current practice, 2nd edition,

CIMA publishing Publication, 2005.

MarekMusiela, MarekRutkowski, Models in Financial Modelling. 2nd

Edition, Springer,

2005.

ChandanSengupta, Financial Analysis and Modeling Using Excel and VBA, Wiley, 2014

Subject: Financial Modelling Blue Print - Question Paper





Name of the Course: Multivariate Data Analysis -1 Duration: One Semester

Semester - 3 Aim of the Course: By the end of the subject, students should be able to understand the

theory behind the statistics, Select the appropriate methods in function of the research

question, Apply those methods to their data, Interpret and report the results from the

analysis, Develop critical thinking of statistics.

Course Overview and Context: The Course seeks to cover the multivariate analysis in

statistical terms and the various types of multivariate techniques so that students can

understand their applications and apply these techniques to the data. The course also

covers factor analysis and multiple regression analysis and how these techniques are useful

in decision making.

Syllabus Content

Unit 1

Overview of Multivariate Statistics 9 hours

What is Multivariate Analysis?, Multivariate Analysis in statistical terms, some basic

concepts of Multivariate Analysis, a classification of Multivariate Techniques, types of

Multivariate Techniques, Canonical Correlation, Guidelines for Multivariate Analyses and

interpretation, a structured approach to Multivariate model building.

Unit 2

Data Cleaning and Multivariate Techniques 9 hours

Graphical examination of the Data, missing data, outliers, testing the assumptions of

Multivariate Analysis, incorporating Nonmetric data with dummy variables, a managerial

overview of the results.

Unit 3

Factor Analysis 9 hours



What is factor analysis, a hypothetical example of Factor Analysis, Factor Analysis decision

process, an illustrative example, a managerial overview.

Unit 4

Multiple Regression 9 hours

What is Multiple regression analysis?, an example of simple and multiple regression

analysis, a decision process for multiple regression analysis, Regression: Mediation &

Moderation, a managerial overview.

Unit 5

Multiple Discriminant Analysis and Logistic Regression 9 hours

What are Discriminant Analysis and Logistic Regression?, analogy ith regression and

MANOVA, hypothetical example of Discriminant Analysis, the decision process for

Discriminant Analysis, logistic regression: regression with a Binary dependent variable,

Dichotomous Dependent Variables, an illustrative example of logistic regression, a

managerial overview.

Competencies of the course C1. Understand Multivariate Analysis in statistical terms.

C2. Understand types of Multivariate Techniques.

C3. Understand the guidelines for Multivariate Analyses and interpretation.

C4. Understand the graphical examination of Data.

C5. Understand structured approach to Multivariate model building.

C6. Test the assumptions of Multivariate Analysis.

C7. Understand the concept of Canonical Correlation.

C8. Understand the classification of various Multivariate Techniques

C9. Understand the basics of factor analysis.

C10. Understand the decision process of factor analysis.

C11. Understand the decision process for multiple regression analysis.

C12. Understand the basics of Multiple regression analysis.

C13. Understand Discriminant Analysis and Logistic Regression.

C14. Understand the decision process for Discriminant Analysis.



C15. Perform logistic regression with a Binary dependent variable and Dichotomous

Dependent.

Learning Resources Hair, J. F. et al. (2006). Multivariate Data Analysis, 6th edition. NJ: Prentice Hall.

Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting

Interactions. Newbury Park, CA: Sage.

Menard, S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.

Tabachnick, B. and Fidell, L (2007). Using Multivariate Statistics, 5th edition. New

York: Allyn & Bacon.

Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the

Behavioural Sciences, 2nd edition. Hillsdale, N.J.: Erlbaum.

Stevens, J. (1992). Applied Multivariate Statistics for the Social Sciences, 2nd edition.

Hillsdale, N.J.: L. Erlbaum Associates.

Subject: Multivariate Data Analysis -1 Blue Print - Question Paper





Name of the Course: Time Series Econometrics Duration: One Semester Semester - 3 Aim of the Course: By the end of the subject, students should be familiar with Univariate

and multivariate models of stationary and nonstationary time series in the time domain

and be able to develop a comprehensive set of tools and techniques for analyzing various

forms of univariate and multivariate time series.

Course Overview and Context: The Course seeks to cover the various time series

components: Irregular, Seasonal & Cyclic Variations & Trend of Time Series, and the

different models of time series which can be used for analysis .The course also involves

understanding the current literature in applied time series econometrics and survey of

some of the current research topics in time series econometrics.

Syllabus Content

Unit 1

Forecasting Time Series 12 hours

Time Series Components: Irregular, Seasonal & Cyclic Variations & Trend of Time Series;

Forecasting through Averaging and Exponential Smoothing; Holt’s model. Forecasting

evaluation through MAD (Mean Absolute Deviation) & MSE (Mean Square Error).

Unit 2

Financial Returns and Volatility Patterns 12 hours

Introduction to financial time series: basic returns data characteristics, asymmetry and

fat tails, historical volatility, volatility estimator used by RISKMETRICS model, high

frequency volatility.



Unit 3

Univariate Time Series Modelling and Forecasting 12 hours

Autocorrelation, stationarity, identification of ARMA models, seasonality, tests of random

walk (weak market efficiency), spurious regression, unit root tests, ARIMA models,

parameter (in)stability.

Unit 4

Multivariate Time Series Models, Modelling Long Run Relationships in Finance 12 hours

Granger causality, Cointegration, VARs: estimation and testing, impulse response and

variance decomposition, error-correction models: estimation and testing, relationship

between international stock indices.

Unit 5

Time Series Volatility ARCH/GARCH Models with Extensions 12 hours

ARCH, GARCH, asymmetric GARCH and other extensions, options implied volatility

versus GARCH, value at risk (VaR), downside risk (expected shortfall ES), credit risk.

Competencies of the course C1. Understand the various Time Series Components: Irregular, Seasonal & Cyclic

Variations & Trend of Time Series.

C2. Understand the technique of forecasting through Averaging and Exponential

Smoothing.

C3. Evaluate forecasting through MAD (Mean Absolute Deviation) & MSE (Mean Square

Error).

C4. Understand financial time series.

C5. Understand the volatility estimator used by RISKMETRICS model.

C6. Understand the concept of historical volatility and high frequency volatility.

C7. Understand univariate and multivariate models of stationary and nonstationary time

series in the time domain.

C8. Understand the tests of random walk (weak market efficiency).



C9. Understand the concept of Granger causality .

C10. Understand the types of error-correction models (estimation and testing).

C11. Understand the relationship between international stock indices.

C12. Understand the concept of downside risk (expected shortfall ES) and credit risk and

its impact.

C13. Understand Value at Risk (VaR) concept and the various methods of calculating VaR.

C14. Distinguish between options implied volatility and GARCH.

C15. Understand ARCH, GARCH, asymmetric GARCH and other extensions.

Learning Resources Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press. Enders, W. (2010). Applied Econometric Time Series. Hoboken, NJ: John Wiley & Sons. Zivot, Eric and Jiahui (Jeffery). (2002). Modeling Financial Time Series with S-PLUS.

Wang: Springer-Verlag.

Brooks, Chris. (2008). Introductory Econometrics for Finance. Cambridge University Press.

Subject: Time Series Econometrics Blue Print - Question Paper





Name of the Course: Predictive Modelling using SAS Duration: One Semester Aim of the Course: By the end of the subject, students should be familiar with the

concepts of a SAS Enterprise Miner project and explore data graphically. Students should

be able to build the predictive models and understand the working of such models. Also

the course will aim at how to generate a score code and how to use it.

Course Overview and Context: The Course seeks to cover the concepts of SAS Enterprise

Miner project . The course involves modifying data for better analysis results, building

and understanding predictive models such as decision trees and regression models,

comparing and explaining complex models, generation of score code and using other

modelling tools such as rule induction, gradient boosting and support vector machines.

Syllabus Content

Unit 1

Introduction 9 hours

Introduction to SAS Enterprise Miner, Accessing and Assaying Prepared Data: Creating a

SAS Enterprise Miner project, library and diagram, Defining a data source, Exploring a

data source.

Unit 2

Introduction to Predictive Modeling with Decision Trees 9 hours

Cultivating decision trees, Optimizing the complexity of decision trees, Understanding

additional diagnostic tools.

Unit 3

Introduction to Predictive Modeling with Neural Networks and Other Modeling

Tools 9 hours



Introduction to neural network models, Input selection, Stopped training, Other

modeling tools.

Unit 4

Model Assessment 9 hours

Model fit statistics, Statistical graphics, Adjusting for separate sampling, Profit matrices,

Model Implementation: Internally scored dataset, Score code modules.

Introduction to Pattern Discovery: Cluster analysis, Market basket analysis .

Unit 5

Special Topics 9 hours

Ensemble models, Variable selection, Categorical input consolidation, Surrogate models.

Competencies of the course C1. Understand the concept of SAS Enterprise Miner.

C2. Understand how to create a SAS Enterprise Miner project.

C3. Understand how to define and explore a data source.

C4. Understand the tools of diagnostic.

C5. Understand the concept of neural network models.

C6. Understand the concept of stopped training.

C7. Understand the Profit matrices

C8. Understand how to build predictive models such as decision trees and regression

models.

C9. Understand the generation and usage of score code.

C10. Understand how to apply association and sequence discovery to transaction data.



C11. Understand the concept of Pattern Discovery along with Cluster analysis and Market

basket analysis.

C12. Understand the process of cultivating decision trees.

C13. Understand how to optimizing the complexity of decision trees.

C14. Understand the model implementation techniques of internally scored dataset and

score code modules.

C15. Understand the concept of Model fit statistics and the various statistical graphics.

Learning Resources

1. Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business

Applications, Second Edition, Edition 2 , Kattamuri S. Sarma, PhD December 19,

2013.

1. Neural Networks & Learning Machines, 3/E English 3rd Edition.

Attendance Policy: Regular attendance mandatory

Subject: Predictive Modelling using SAS Blue Print - Question Paper




Course Code : CO4B19TM Name of the Course: Big Data Analytics Duration: One Semester 2:1:0

Aim of the Course: By the end of the course, the student will be able to deploy a

structured lifecycle approach to data science and big data analytics projects. The course

is also designed with the aim that students should be able to select techniques and tools

to analyze big data and create statistical models.

Course Overview and Context: The Course seeks to cover the concepts of big data, the

various techniques and tools available to analyze the big data, creation of statistical

models, the distinguishing factors between Hadoop and BigData. The course also focuses

on Hadoop Distributed File System, its architecture and data flow.

Unit 1

Introduction 9 hours Big Data Overview, Definition with Real Time Examples, How BigData is generated with Real Time Generation, Use of BigData, Future of BigData!, the challenges for processing big data, technologies supporting big data,

Unit 2

Hadoop 9 hours Why Hadoop?, What is Hadoop?, Hadoop vs RDBMS, Hadoop vs BigData, Brief history of Hadoop, Problems with traditional large-scale systems, Requirements for a new approach, Anatomy of a Hadoop cluster

Unit 3

Hadoop Distributed File System (HDFS) 9 hours Concepts & Architecture, Data Flow (File Read , File Write), Fault Tolerance, Shell Commands, Java Base API, Data Flow Archives, Coherency, Data Integrity, Role of Secondary NameNode

Unit 4

MapReduce 9 hours Theory, Data Flow (Map – Shuffle - Reduce), MapRed vs MapReduce APIs, Programming [ Mapper, Reducer, Combiner, Partitioner]

Unit 5



HIVE, PIG and HBase 9 hours Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Why Pig, Use case of Pig, Pig Components

Data Model, Pig Latin

RDBMS Vs NoSQL, HBase Introduction, HBase Components Scanner, Filter Hbase POC, Introduction to MongoDB.

Competencies of the course C1. Understand how Big Data is generated with Real Time Generation.

C2. Understand the challenges for processing big data.

C3. Understand the technologies supporting big data.

C4. Understand the concept of Hadoop.

C5. Distinguish Hadoop with RDBMS and BigData.

C6. Understand the anatomy of a Hadoop cluster.

C7. Understand what Hive Web Interface is.

C8. Distinguish RDBMS with NoSQL.

C9. Understand the architecture, installation, and configuration of HIVE, PIG and HBase.

C10. Understand the need for PIG, its usage and components.

C11. Understand the concept of Hbase, its components scanner and carry out the

filtration for Hbase POC

C12. Understand the concept of MongoDB.

C13. Understand the concept of MapReduce and its data flow.

C14. Distinguish MapReduce APIs with MapRed

C15. Understand the step by step process of programming under MapReduce.

Learning Resources



1. Tan, Pang-Ning, Steinbach, Michael and Kumar, Vipin. (2005). Introduction to Data

Mining. Addison-Wesley.

1. Lin, Jimmy and Dyer, Chris. (2010). Data-Intensive Text Processing with MapReduce.

Morgan & Claypool Publishers.

2. Rajaraman, Anand and Ullman, Jeff. (2008). Mining of Massive Datasets. New York:

Cambridge Press.

3. Han, Jiawei and Kamber, Micheline. (2000). Data Mining: Concepts and Techniques.

The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor

Morgan Kaufmann Publishers.

Subject: Big Data Analytics





COURSE CODE: CO4B20TM

Name of the Course: Multivariate data Analysis – 2 Duration: One Semester Aim of the Course: By the end of the subject, students should be able to understand the

theory behind the statistics, Select the appropriate methods in function of the research

question, Apply those methods to their data, Interpret and report the results from the

analysis, Develop critical thinking of statistics.

Course Overview and Context: The Course seeks to cover the concepts of Cluster Analysis,

using cluster analysis as a multivariate technique, the necessity of conceptual support in

Cluster Analysis. The course also focuses on Multidimensional Scaling and MANOVA

analysis and how these tools are useful for decision making process.

Syllabus

Unit 1

Cluster Analysis 9 hours

What is Cluster Analysis, Cluster Analysis as a multivariate technique, conceptual

development with Cluster Analysis, necessity of conceptual support in Cluster Analysis,

how does Cluster Analysis work? Objective versus subjective considerations, Cluster

Analysis decision process, objective of Cluster Analysis, research design in Cluster

Analysis, assumptions in Cluster Analysis, deriving clusters and assessing overall fit,

interpretation of the clusters, validation and profiling of the clusters, an illustrative

example.

Unit 2

Multidimensional Scaling and Correspondence Analysis 9 hours

What is Multidimensional Scaling? MDS works, comparing MDS to other

interdependence techniques, a decision framework for perceptual mapping Overview of

Multidimensional Scaling, Correspondence Analysis, illustrations of MDS and

Correspondence Analysis.



Unit 3

MANOVA 9 hours

MANOVA: Extending Univariate Methods for Assessing Group Differences, a hypothetical

illustration of MANOVA, a decision process for MANOVA, illustration of MANOVA

analysis, example 1: difference between two independent groups, example 2: difference

between K independent groups, example 3: a factorial design for MANOVA with tow

independent variables, a managerial overview of the results.

Unit 4

Structural Equation Modeling Part I 9 hours

What is Structural Equation Modeling?, SEM and other multivariate techniques, the role of

theory in structural equation modelling, history of SEM, a sample example of SEM,

developing a modelling strategy, six stages in structural equation modelling, what is

confirmatory factor analysis, SEM stages for testing measurement theory validation with

CFA, advanced topics in CFA, advanced CFA illustration.

Unit 5

Structural Equation Modeling Part II 9 hours

What is structural model, a sample example of structural model, an overview of theory

testing with SEM, stages in testing structural theory, SEM illustration.

Competencies of the course C1. Understand the concept of cluster analysis and usage of cluster analysis as a

multivariate technique.

C2. Understand conceptual development with Cluster Analysis.

C3. Understand the necessity of conceptual support in Cluster Analysis.

C4. Understand the concept of Multidimensional Scaling and its working.

C5. Distinguish MDS to other interdependence techniques.

C6. Understand how to extend univariate methods for assessing group differences.



C7. Understand how the decision process for MANOVA is outlined.

C8. Understand the stages in testing structural theory.

C9. Understand the concept of Structural Equation Modeling.

C10. Understand SEM and other multivariate techniques.

C11. Understand the role of theory in structural equation modeling.

C12. Understand the decision process of cluster analysis.

C13. Understand the research design in Cluster Analysis.

C14. Understand the decision framework for perceptual mapping.

C15. Understand the concept of Correspondence Analysis.

Learning Resources 1. Hair, J. F. et al. (2006). Multivariate Data Analysis, 6th edition. NJ: Prentice Hall.

1. Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and Interpreting

Interactions. Newbury Park, CA: Sage.

2. Menard, S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.

3. Tabachnick, B. and Fidell, L (2007). Using Multivariate Statistics, 5th edition. New

York: Allyn & Bacon.

4. Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for

the Behavioural Sciences, 2nd edition. Hillsdale, N.J.: Erlbaum.

5. Stevens, J. (1992). Applied Multivariate Statistics for the Social Sciences, 2nd

edition. Hillsdale, N.J.: L. Erlbaum Associates.

Attendance Policy: Regular attendance mandatory

Instruction schedule: As notified by the Department

Assignments: Two tutorial Assignments, One class Test and the Final Examination at

the end of the Semester



Subject: Multivariate Data Analysis – 2



COURSE CODE CO4B21TM

Name of the Course: Analytics with R Duration: One Semester Aim of the Course: By the end of the subject, students will learn how to program in R and

how to use R for effective data analysis. Students will be able to perform analysis using R

in various fields like Inferential Statistics, Regression and Recursive Partitioning,

Survival Analysis, Meta-Analysis.

Course Overview and Context: The Course seeks to cover the concepts of Inferential

Statistics, regression analysis which has sub divisions like Simple and Multiple Linear

Regression, Logistic Regression and Generalised Linear Models, Recursive Partitioning,

Survival Analysis, Meta-Analysis and how each of thses can be ananlyzed using R.

Syllabus Content

Unit 1

Introduction to R 9 hours

An Introduction to R: What is R?, Installing R, Help and Documentation, Data

Objects in R, Data Import and Export, Basic Data Manipulation, Computing with

Data, Organising an Analysis.

Data Analysis Using Graphical Displays: Introduction, Initial Data Analysis,

Analysis Using R.



Unit 2

Inferential Statistics 9 hours

Simple Inference: Introduction, Statistical Tests, Analysis Using R.

Conditional Inference: Introduction, Conditional Test Procedures, Analysis Using

R.

Analysis of Variance: Introduction, Analysis of Variance, Analysis Using R.

Unit 3

Regression and Recursive Partitioning 9 hours

Simple and Multiple Linear Regression: Introduction, Simple Linear Regression,

Multiple Linear Regression, Analysis Using R.

Logistic Regression and Generalised Linear Models: Introduction, Logistic

Regression and Generalised Linear Models, Analysis Using R.

Density Estimation: Introduction, Density Estimation, Analysis Using R.

Recursive Partitioning: Introduction, Recursive Partitioning, Analysis Using R.

Smoothers and Generalised Additive Models: Introduction, Smoothers and

Generalised Additive Models, Analysis Using R.

Unit 4

Survival Analysis 9 hours

Introduction, Survival Analysis, Analysis Using R.

Analysing Longitudinal Data: Introduction, Analysing Longitudinal Data, Linear

Mixed Effects Models, Analysis Using R, Prediction of Random Effects, The

Problem of Dropouts.

Analysing Longitudinal Data II: Introduction, Methods for Non-normal

Distributions, Analysis Using R: GEE, Analysis Using R: Random Effects.

Simultaneous Inference and Multiple Comparisons: Introduction, Simultaneous

Inference and Multiple Comparisons, Analysis Using R.



Unit 5

Meta-Analysis 9 hours

Introduction, Systematic Reviews and Meta-Analysis, Statistics of Meta-Analysis,

Analysis Using R, Meta-Regression, Publication Bias.

Principal Component Analysis: Introduction, Principal Component Analysis,

Analysis Using R.

Multidimensional Scaling: Introduction, Multidimensional Scaling, Analysis Using

R.

Cluster Analysis: Introduction, Cluster Analysis, Analysis Using R.

(The public domain software R will be used extensively throughout the course. This

software can be freely downloaded from http://www.r-project.org/.)

Competencies of the course C1. Understand what is R and its working.

C2. Understand Data Analysis Using Graphical Displays and how to analyze using R.

C3. Understand simple and conditional inference and how to analyze using R.

C4. Understand Simple and Multiple Linear Regression and how to analyze using R .

C5. Understand the concept of Smoothers and Generalised Additive Models and analyze

using R.

C6. Understand the concept of Density Estimation and analyze using R.

C7. Understand the concept of Recursive Partitioning and analyze using R.

C8. Understand Logistic Regression and Generalised Linear Models.

C9. Understand the analysis of Longitudinal Data.

C10. Understand the concept of Survival Analysis and analyze using R.

C11. Understand Statistics of Meta-Analysis and analyze using R.

C12. Understand the concept of Systematic Reviews and Meta-Analysis.

C13. Understand the concept of Principal Component Analysis and analyze using R.



C14. Understand the concept of Multidimensional Scaling and analyze using R.

C15. Understand the concept of Cluster analysis and analyze using R.

Learning Resources Textbook 2. Dalgaard, Peter. (2008). Introductory Statistics with R, 2nd edition. California:

Springer.

References 1. Chambers, M. John. (2008). Software for Data Analysis: Programming with R (Statistics

and Computing). California: Springer.

2. Hothorn, Torsten and Everitt, Brian S. (2005). A Handbook of Statistical Analyses

Using R, Second Edition. Chapman and Hall.

3. Albert, Jim and Rizzo, Maria. (2012). R by example: Concepts to Code. California:

Springer.

4. Allerhand, Mike. (2011). Tiny Handbook of R. California: Springer.

Subject: Analytics with R





Course code : CO4B22TM

Name of the Course: Data Mining for Business Analytics Duration: One Semester Aim of the Course: The course aims at familiarizing the students with the goals and

objectives of data mining. The main objective of the course is to teach the students how

to conduct a data mining project and obtain practical experience in designing and

implementing data mining algorithms.

Course Overview and Context: The course familiarizes the students with popular

classification techniques, such as decision trees, support vector machines and nearest-

neighbor approaches. The course also includes the most important association analysis

techniques, the complete data mining process and the various methods of data mining.

Data mining software tools will be introduced to students as part of their curriculum.

Syllabus Content Unit 1

Introduction to Data Mining 12 hours

Data Mining for Business Intelligence, Data Mining Goes to Hollywood!, Data Mining

Concepts and Definitions, Definitions, Characteristics, and Benefits, How Data Mining

Works, Data Mining Applications.

Unit 2

Data Mining Process 12 hours

Data Mining Process, Step 1: Business Understanding , Step 2: Data Understanding, Step

3: Data Preparation, Step 4: Modeling Building, Step 5: Testing and Evaluation, Step 6:

Deployment, Other Data Mining Standardized Processes and Methodologies.

Unit 3

Data Mining Methods 12 hours

Data Mining Methods, Classification, Estimating the True Accuracy of Classification

Models, Cluster Analysis for Data Mining.

Unit 4

Artificial Neural Networks 12 hours



Association Rule Mining, Artificial Neural Networks for Data Mining, Elements of ANN,

Applications of ANN.

Unit 5

Data Mining Software Tools 12 hours

Data Mining Software Tools, Data Mining Myths and Blunders.

Competencies of the course C1. Understand Data Mining for Business Intelligence.

C2. Understand how data mining works.

C3. Understand the applications of Data Mining.

C4. Understand the various steps of data mining process.

C5. Understand other data mining standardized processes and methodologies.

C6. Understand the Cluster Analysis for Data Mining.

C7. Understand the methods of Data Mining.

C8. Understand the elements and application of ANN.

C9. Estimate the true accuracy of classification models.

C10. Understand Artificial Neural Networks for Data Mining.

C11. Recognize the data mining myths and blunders.

C12. Understand the Data Mining Software Tools.

C13. Understand the classification of various data mining methods.

C14. Understand Association Rule Mining.

C15. Understand the various characteristics and benefits of data mining.

Learning Resources 1. Turban, Sharda Efraim; Ramesh, Dursun Delen and King, David. (2011). Business

Intelligence: A Managerial Approach, 2nd Edition. Publisher: Prentice Hall.

1. Han, Jiawei and Kamber, Micheline. (2012). Data Mining: Concepts and Techniques,

3rd edition. Morgan Kaufman Publishers.

2. Tang, P.N., Steinback, M. and Kumar, V. (2006). Introduction to Data Mining. Addison

Wesley.

3. Myatt, Glenn and Johnson, Wayne. (2009). Making Sense of Data II. John Wiley& Sons. 4. Rajaraman, Anand. (2011). Mining of Massive Datasets. New York: Cambridge

University Press.



Subject: Data Mining for Business Analytics



Documents

PGDM Business Analytics Duration of the Course: 2 Years ... · PGDM – Business Analytics Duration of the Course: 2 Years Full-time (4 semesters) ... IBM, Accenture, Infosys have