GALGOTIAS BUSINESS SCHOOLPOST GRADUATE DIPLOMA IN MANAGEMENT

COURSE OUTLINEREVISED IN APRIL 2012

Course Title: Data Warehouse and Data Mining PGDM Batch: PGDM 2011-13, Trimester: VInstructor: Prof. Nidhi S. Natrajan

1.0 Course Description: As the variety, velocity and volume of data generates huge information repository, it is getting increasingly difficult for the organizations to draw conclusion from it. Past decision support systems i.e. scheduled reporting & generic data visualization techniques failed to deliver varied informational needs and gave way to modern technologies i.e. datawarehousing and data mining. Data warehouse and data mining satisfy the needs of an organization across segments and hierarchy.

2.0 Course Objective:2.1 The aim of the course is to provide a sound understanding of data warehousing systems, data mining tools & techniques and visualization.2.2 To learn the use of excel based analysis.

3.0 Course Pre-requisite: Knowledge of MIS and DBMS.

4.0 Course Outcome: The students will be able to :4.1 learn the use of tools, techniques and applications in Datawarehousing and data mining.

. 4.2 Develop an understanding of the power of Excel in allowing both analysis of business data sets and in the flexible preparation of graphs, charts and tables for inclusion in reports.

5.0 Prescribed Text:

David Whigham, “Business Data Analysis using Excel”, Oxford Higher Education.

6.1 Additional References:

Reema Thareja, “Data warehousing”, Oxford Higher Education Pradeep Hari Pendse, “Business Analysis”, PHI A B M Shawakat Ali and Saleh A. Wasmi.”Data Mining: Methods and Techniques”,

Cengage Berson, “Data Warehousing, Data-Mining & OLAP”, TMH

6.2 Journals: IGI Global: International Journal of Datawarehousing and mining (available online)

6.3 Magazines / websites / Newspapers: www.tdwi.org/ www.ibm.com/software/data/infosphere/warehouse/

7.0 Pedagogy: Lectures Quizzes Case study Lab test

8.0 Evaluation Scheme:

8.1 Continuous Evaluation:

Quizzes ( 3-4) 20 Marks [one quiz from reference book]

Case study test 05 Marks Group Presentation + Report 10 Marks Lab test 05 Marks

8.2 Centralized Evaluation

Mid-term examination 20 marks End-term examination 40 marks

Total 100 marks

8.3 Project to study how a company got benefited by the use of data warehousing. Project will be evaluated on milestones.

Name of group and organization – session 3 First set of information collected – session 10 Submission of report -- session 27 Presentation -- session 29/30

9.0 Session wise instruction plan:

Session Module Topics Core Reading

Additional Reading

1 Introduction History and need of data warehousing and data analysis

Case Study: Business Organization –A car hire company(PHP)

Hand outRT-1, PHP-1

2-5 Data warehousing defining featuresAnd Architecture of a Data warehouse

Difference between DBMS and warehousing

Features of data warehousing Information flow mechanism Data flow from warehouse to

operational system Characteristics of data

warehouse Data warehouse and data mart Pushing and pulling of data Case study: Warehouse

usecase for Bank ATM(PHP)

Hand out RT-2, Ali-2

6-7 Data warehouse schema

Gathering business data Designing schema Case study on Wall Mart

DW-2, Hand out

PHP-4

8 Building a data warehouse

Problem definition Requirement analysis Planning Building and

implementation Backup and recovery Quiz on above

Self study RT-11

9-10 Business Analysis and prerequisite for Business Analyst

Emergence of business Analysis

Emerging role of Business Analyst

Careers for a Business Analyst

Hand out

11-12 Data Mining basics

Introduction Architecture of data

mining system Knowledge discovery

process

Hand out RM-12, Ali-8

13 Examples on various industries

Finance, Insurance, Telecommunication, Transport, Consumer

notes

goods, Data service providers etc

14OLAP in the data warehousing

Need of OLAP OLAP(online analytical

processing) tools Data cube ROLAP(relational OLAP),

MOLAP(multidimensional OLAP)

Slicing and dicing

Hand out RM-10

15 Classification , Association, Clustering

Defining classification Characteristics of classification Use of decision tree in

classification Decision tree algorithm Mining for association rule Clustering techniques Mining sequence data

Hand out

MID TERM16 Moving into

Mining and Latest trends in data warehousing

How to categorize data mining system

Interesting and useful data

Applications of data mining

Data Visualization Web-enabled data

warehouse Data warehouse future Ethical use of data Case study on

RBI(discussion after self reading)

Hand out RM-13

17-18 Introduction to data handling using excel

Sorting Filtering Parsing

DW-1,2,3

19--22 Elementary modelling

Symbols, expression and simple models

Linear functions in business Linear functions with logical

test Vertical lookup function and

Horizontal lookup

DW-5.6

23-24 Association Data description Association Pearson’s correlation

DW-11

coefficient

25 Regression Regression using the Excel Data Analysis routine

DW-12

26-27 Financial arithmetic

The equivalent annual rate(YOY)

Present value and discounting multiple amounts.

DW-13,14

28 Excel test29-30 Wrap up session and presentation

END TERM EXAM

10.0 Faculty contact hours: