Welcome to IS417

Data Warehousing and Business Analytics (DWBA)

About

Data warehousing has recently gained a considerable momentum as a fundamental paradigm for driving daily business analytics operations.

This course is designed to provide an introduction to fundamental issues and novel techniques of data warehouse. Issues covered include

data warehouse planning; business modeling, design, and implementation. In particular, the role of data warehouse in supporting business

intelligence (BI) and effective decision making is also reviewed and explored.

 

This course is taught with an emphasis to balance theories about data warehouse techniques and applications to real world BI problems.

The participants will explore applications and have unique opportunity for hands-on exposure with business analytics for data

warehousing using the advanced software packages from leading industrial partners.

Staff & Office Hours

Instructor

Professor Jialie SHEN (Jerry)

Tel

6828-0982 (o)

Email

jlshen@smu.edu.sg

Consultation

Every Friday        1:00pm ~ 2:00pm      Rm 80-5025

TA

Leonard NG Wee Siong

Email: leonard.ng.2008@sis.smu.edu.sg

Consultation: Every Friday, 14:00 ~ 15:00, Week 2 ~ 13,

SIS GSR 3-2 (Rm 3015)

 

Teaching Strategies

  • Lectures/Invited Talks: the main way to introduce concepts, show examples
  • Lab/Demo: work on projects or examples. Instructor is available in labs.
  • Consultations: weekly one-hour sessions to provide personalized advice to students.

The lecutres will serve as the main method of delivering the course contents to the students. Weekly laboratories,

assignments and projects give the students the hands-on experience on various subjects of the coruse. The consultation

will give students the opportunities to seek the assistance from the teaching staffs over all contents of the course.

 

Course Assessment

This course’s assessment consists of 2 problem sets, attendance & participation, one term project, one mid-term exam

and one final exam. Grades will be determined on the following basis:

 

Problem set

10%

2 sets, 5% each, individual

Attendance & Participation

10%

 

Term project

20%

Final presentation 10%, Final report 10%, group based

Mid-term exam

20%

90 mins, written exam, week 7

Final exam

40%

3 hours, written exam

 

Final mark (100%) = Problem set + Term project + Attendance & Participation +

                                   Mid-term exam + Final exam

 

 

Course Schedule

WK/DATE

Class Topic

Readings

Remark

1

Course Overview

 

 

 

Part I: Basic Knowledge about Data Warehousing and BI

 

 

 

 

1

[Jan 13]

 

Overview of Data Warehousing & BI

 

Invited Talk I by SAS

[CB01]

Chp.1 pp. 4 ~ 11

 

[CB02]

Chp. 3 pp. 105 ~ 110

 

[RA04]

 

 

2

[Jan 20]

 

Dimensional Modeling I: Basics

[CB02]

Chp. 3 pp. 110 ~ 122

 

[CB03]

Chp. 6 pp. 233 ~ 248

 

 

3

[Jan 27]

 

Dimensional Modeling II: Advanced Topics

 

Lab 1: Dimensional Modeling

 

[CB03]

Chp. 6 pp. 253 ~ 267

 

Problem set 1 out

 

4

[Feb 3]

 

Online Analytical Processing (OLAP)

 

Lab 2: OLAP analysis and Reporting 

[CB02]

Chp. 3 pp. 123 ~ 126

            pp. 135 ~ 136

 

[RA07]

 

Deadline for group registration

 

Part II: Data Warehouse Architecture and Management

 

 

 

 

5

[Feb 10]

 

Extract, Transform, Load  (ETL) and Data Quality

 

Lab 3a: ETL process

 

[CB01]

Chp. 10 pp. 256 ~ 259

             pp. 267 ~ 276

             pp. 281 ~ 283

 

[RA05]

 

 

 

6

[Feb 17]

 

Data Warehouse Architecture

Data Warehouse Development and Management

 

Tutorial I

 

Lab 3b ETL process

 

[CB02]

Chp. 3  pp. 130 ~ 133

            

[CB01]

Chp. 9  pp. 236 ~ 239

             pp. 246 ~ 249

 

[RA01]  [RA02] 

 

 

7

[Feb 24]

Mid Term Examination (90 mins, Close book) & Term project consultation

 

Problem set 2 out

and

Problem set 1 due

8

Session break (Feb 27 ~ Mar 4)

 

 

 

Part III: Applications of DW/BI Systems

 

 

 

 

 

9

[Mar 9]

 

Decision making and Business Intelligence I  – Basics

 

Team presentation for term project [Phase I]

 

SAS Invited Talk II

[CB03]

Chp. 11 pp. 473 ~ 479

 

[CB02] 

Chp. 1  pp. 21 ~ 27

 

[RB04]

Chp.1  pp. 22 ~ 28

 

 

 

 

10

[Mar 16]

 

Decision making and Business Intelligence II – Turning data to firm performance

 

Tutorial II

 

Lab 4: SAS BI/DW introduction

 [CB02]

Chp. 11 pp. 649 ~ 653

 

[RA06] 

 

 

11

[Mar 23]

 

Next Generation Data Warehousing and BI

 

Lab 5: SAS data integration toolkit

 

Hand out & class Slides

 

 

12

[Mar 30]

Team project presentation

 

Problem set 2 due

13

[Apr 6]

Team project presentation

 

 

14

Student Study Week

 

 

15

Final Examination

Date: 20 Apr 2012  (Friday)  Time: 14:30 - 17:30

 

 

 

Problem Sets

·         Problem set 1

·         Problem set 2

Projects (TBA)

 

Textbooks & Reference

Core Text Books:

·      [CB01]  The Data Warehouse Mentor: Practical Data Warehouse and Business Intelligence Insights, R. Laberge, 2011, McGraw-Hill.

·      [CB02]  Data Mining: Concepts and Techniques, J. Han and M. Kamber, 2nd edition, 2006, Kaufmann Publishers

·      [CB03] The Data Warehouse Lifecycle Toolkit: Practical Techniques for Building Data Warehouse and Business Intelligence Systems,

R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, B. Becker, 2nd  edition, 2007, Wiley

Reference Books:

·      [RB01] Fundamentals of Data Warehouses, M. Jarke, M. Lenzerini, Y. Vassiliou, P. Vassilidis, 2nd  edition, 2003, Springer

·      [RB02] Database Management Systems, R. Ramakrishnan and J. Gehrke, 3rd edition, 2002, McGraw-Hill

·      [RB03] Information Rules – A Strategic Guide to the Network Economy, C. Shapiro and H. R. Varian, Harvard Business School Press, 1998.

·       [RB04] Data Mining: Practical Machine Learning Tools and Techniques, I. H. Witten and E. Frank, 2nd Edition, Elservier 2005

Reference Articles:

·      [RA01]  My Top 10 Assertions about Data Warehouses, M. Stonebraker, BLOG@CACM, 2010.

·      [RA02]  Managerial Considerations, H. J. Watson and B. J. Haley, Communications of the ACM, Vol. 41, No. 9, 1998.

·      [RA03] Does Data Warehouse End-User Metadata Add Value? N. Foshay, A. Mukherjee, A. Taylor, Communications of the ACM, Vol. 50, No. 11, 2007.

·      [RA04] An Overview of Business Intelligence Technology,  S. Chaudhuri, U. Dayal,  V. Narasayya, Communications of the ACM, Vol. 54, No. 8, 2011.

·      [RA05] Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian product,

            A. Parssian, S. Sarkar and V. S. Jacob, Management Science, Vol. 50, No. 7, 2004.

·      [RA06]  Competing on Analytics, Thomas H. Daveport, Harvard Business Review, January, 2006.

·      [RA07] Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals,

             Data Mining and Knowledge Discovery, J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, Vol. 1, No. 1. 1997.

Rubrics (TBA)