Course Expectations and Tentative Syllabus

 

CIS:624                              Data Warehousing                                                                                         Fall 2004

                                             Bucks Campus Room 125                                                                            Tu  6:15-9:00pm

 

Professor:                          Dr. Michael Redmond   

                                             330 Olney Hall  (215) 951-1096

                                             redmond@lasalle.edu

                                             http://www.lasalle.edu/~redmond/624

 

Office Hours:                    Tu  5:00-6:00pm

                                            And at other times by appointment. Also, by phone and e-mail.

 

Text:

               Kimball, R., Reeves, L., Ross, M., and Thornthwaite, W., The Data Warehouse Lifecycle Toolkit, Third Edition, Wiley, 1998

 

Course Description:

               Data Warehousing is a popular and growing area involving the use of large scale data stores to support business decision-making. This course is intended to introduce the student to the critical success factors in designing and implementing a data warehouse. The textbook is geared toward people who will be applying the ideas in their organization – i.e. it is geared toward the practitioner not the theoretician.  While we are in some ways limited in our hands-on possibilities due to the size of realistic data, and costs of realistic tools, there should be hands-on opportunities with OLAP software. It is anticipated that we will do some role-play of situations in order to make other parts of the course come to life.

Topics to be covered include management, requirements analysis, design, infrastructure, data staging, data access, and data mining.  Data mining is largely outside of the scope of the text, so supplemental readings will be identified.

The course assumes knowledge of database concepts, particularly relational database concepts. The text assumes some familiarity with client-server ideas (but not practice).

 

Grading:

 

   Midterm                                                         20%

   Final Exam                                                     35%

   Design Assignment                                       10%

   OLAP Assignment                                        10%

   Data Staging Assignment                              10%

   Aggregates and Disk Space Assignment     10%

   Class Participation                                          5%

 

   Grade Scale:

               A            92-100

A-               90-91

B+          88-89

B            82-87

B-           80-81

C            60-79

F             < 60

No make up exams unless arranged in advance.

Final exam is cumulative, but will focus more heavily on the (previously untested) final half of the course.

There will be several, varied assignments over the course of the semester. One will involve using Cognos PowerPlay OLAP software. This software is accessible over the WWW so should be able to be used outside La Salle. A second assignment will involve designing a hypothetical data mart. A third  will involve estimating disk space requirements for a data mart and the impact of aggregates. A fourth is still to be determined, but should involve data staging. The assignment due dates will be specified when they are assigned.

 

 

                Course Objectives

 

Concepts:

 

1. The student should understand the benefits of database warehousing.

 

2. The student should understand the basic elements in the data warehouse.

 

3. The student should understand the phases in the data warehouse lifecycle. 

 

4. The student should understand the basic issues in data warehouse project management.

 

5. The student should understand the process of data warehouse requirements analysis.

 

6. The student should understand the principles of dimensional modeling using star schemas.

 

7. The student should understand the issues involved in staging data from operational systems into the data warehouse, including data extraction, transformation, cleansing, and building aggregates.

 

8. The student should understand the issues involved in providing warehoused data to business users to support decision making.

 

9. The student should understand the issues involved in determining infrastructure needs to support a data warehouse

 

10. (time permitting) The student should understand the use of data mining on warehouse data, and requirements mining puts on the warehouse.

 

 

 

Applications:

 

1. The student should gain some exposure and experience with a commercial OLAP tool.

 

2.  The student should gain experience creating a logical design for a data mart.

 

3. The student should learn about the different categories of tools related to data warehousing currently available.

 

 

 


 

Tentative Course Plan:

 

 

Date                      Material                                                             Reading

 

Aug 31                  Intro to Class,

                              Basic Elements of Data Warehouse                              Chapt 1

                              A Sample OLAP based Application

 

Sept 7                   Data Warehouse Lifecycle                                             Chapt 2

 

Sept 14                 OLAP Software                                

 

Sept 21                 Project Planning and Management,                Chapt 3

Requirements Analysis                                    Chapt 4

 

Sept 28                 Dimensional Modeling                                    Chapt 5

 

Oct 5                     Dimensional Modeling                                    Chapt 5

 

Oct 12                  Dimensional Modeling                                    Chapt 7

 

Oct 19                  MIDTERM                                                       

                                            

Oct 26   NO CLASS – FALL  BREAK

 

Nov 2                    Data Warehouse Architecture                         Chapt 8

                              Back Room                                                       Chapt 9

 

Nov 9                    Back Room                                                       Chapt 14

 

Nov 16                  Back Room                                                       Chapt 16

 

Nov 23                  Data Staging Software                                     

 

Nov 30                  Front Room                                                      Chapt 10, Chapt 17            

 

Dec 7                    Data Mining                                                      supplemental

 

Dec 14                  Final Exam