Course Design Document. IS417: Data Warehousing and Business Analytics

Transcription

1 Course Design Document IS417: Data Warehousing and Business Analytics Version June 2009 IS417 Data Warehousing and Business Analytics Page 1

2 Table of Contents 1. Versions History Overview of the Data Warehousing and Business Analytics Course Output and Assessment Summary Group Allocation for Assignments Learning Outcomes, Achievement Methods and Assessment Classroom Planning Course Schedule Summary List of Information Resources and References Tooling Weekly Plan IS417 Data Warehousing and Business Analytics Page 2

3 1. Versions History Version Description of Changes Author Date V1.0 Jialie Shen and Steven Miller V2.0 Update course document based on experience of Jialie Shen, the first delivery. Major changes include Swapna G., Adding details of revised structure for the Steven Miller and course Sudip Majumder Adding text book and reference information Revising weekly plan and contents V2.1 Major changes include: Refine weekly arrangement and course goal Restructure course architecture Refine assessment structure Jialie Shen, Swapna G., Steven Miller IS417 Data Warehousing and Business Analytics Page 3

4 2. Overview of the Data Warehousing and Business Analytics Course 2.1 Synopsis Data warehousing has recently gained a considerable momentum as a paradigm for driving daily business analytics operations. This course provides an introduction to fundamental issues and novel techniques of data warehouse. Issues covered in this learning experience include data warehouse planning; business analytics modeling, design, and implementation. In particular, the role of data warehouse in supporting business intelligence and effective decision making is emphasized through labs, projects and case studies. The course is designed to expose students to concepts, enabling methods and hands-on usage and problem solving in an integrated way. As one of IS depth electives, it provides a good balance between theory and practice. The participants will explore applications and have great opportunity for hands-on experimentation with data warehousing using advanced software packages from leading industrial vendors. 2.2 Prerequisites IS202 Data Management or equivalent with approval from instructor 2.3 Objectives Our objectives are to provide you with broad coverage and examples about data warehouse techniques and trends underlying current and future development. In particular, through this course, participants will: Gain an understanding of basic data warehousing applications and techniques, and how data warehousing enables business intelligence capabilities that are used across many industries. Learn how to combine and consolidate data from the various databases scattered throughout a company into a data warehouse. Learn how data inside a data warehouse is organized into a data cube. Explore how to use the data cube to do business analytics and reporting. This includes how to slice and dice the data to get different views of the information; how to aggregate and disaggregate the data to see the information with varying degrees of resolution; and how to do important types of business analytics and related reports. Acquire hands-on experience with key components of an integrated data warehousing and business intelligence system using a leading industry commercial application package. IS417 Data Warehousing and Business Analytics Page 4

5 Use data warehousing/bi applications to create enterprise business intelligence and analytics applications for solving real world problems. Study best practices and case studies for using data warehousing applications, data warehousing enterprise platforms, and integrated data warehousing/business Intelligence applications. Gain highly desired IT and business application skills for using data warehousing to create business intelligence solutions to meet real world needs. 2.4 Basic Modules Basic modules can be found in below figure Business Intelligence and Text Social Processing Network Applications Related Applications Text Data OLAP Text Warehousing Retrieval & Mining OLTP Techniques Techniques Case Studies, Best Practices & Innovations Project, Assignment and Lab Session Detail modules of this course can be found in below figure Data Warehousing Techniques OLAP &OLTP Techniques Assignment Lab Tutorial Case Study Data Warehouse Architecture Extract, Transform Load (ETL), Performance Tuning Data Warehouse Design Multidimensional DB Dimensional Modeling Data Cube Business Intelligence and Business Analytics Applications Data Mining & OLAP/OLTP IS417 Data Warehousing and Business Analytics Page 5

6 Main knowledge points and their relationship in this course can be illustrated in the below figure, Data quality and ETL Business Intelligence Applications DW architecture Data cube, mining and OLAP Design and Modeling 2.5 Instructional Staff: Teaching staff: Jialie Shen, Swapna Gottipati Course Advisor: Prof. Steven Miller, Dean of SIS, SMU Sudip Majumder, Senior director of BI department, Oracle US 3. Output and Assessment Summary In order to evaluation teaching quality and learning result, different kind of assessment methods is used for this course. The detail information is as below. Week Date Output Assessments Lab Weighting 1 Warm-up Lab Session 2 3 In-Class Lab 1 4 Assignment 1 In-Class Lab 2 10% 5 In-Class Lab 3 6 Project Doc. Release In-Class Lab R E C E S S 9 Mid Term Exam Assignment 2 In-Class Lab 5 15% 10% In-Class Lab Project Presentation 10% 14 Project Report 15% 15 Final Exam 30% Participation 10% Total 100% IS417 Data Warehousing and Business Analytics Page 6

7 3.1 Participation (10%) In-class discussion: 5% Presentation skill: 2% Contribution to the learning of the class: 3% 3.2 Assignment (20%) Assignment exercises: 20% 2 assignments Assignment 1 (10%) and Assignment 2 (10%). 3.3 Project (25%) The project is intended to complement the class materials, by getting students to investigate selected topics in greater depth or breadth. The project can be done individually, or in pairs. Teams should produce output that is proportionally higher in quality or quantity. The project report and presentation will contribute15% and 10%, respectively, of the course grade. In addition, project will be group based and topics focus on BI or BA applications with data warehousing techniques. The size of project group can be 3 ~ 4 members. Every team should produce output that is proportionally higher in quality or quantity. 3.4 Test & Exam (45%) Mid term exam carries 15% The final exam (2 hours) carries 30%. 4. Group Allocation for Assignments As mentioned before, there are two written assignments for this course. Both are individual-based. Assignment No How groups are formed? No of Students in a group Assignment 1 Students form the group 1 Assignment 2 Students form the group 1 IS417 Data Warehousing and Business Analytics Page 7

8 5. Learning Outcomes, Achievement Methods and Assessment 1 IS417 Data Warehousing and Business Analysis Integration of Business & Technology in a sector context 1. Business IT Value Linkage skills YY 2. Cost & Risk Analysis skills Y 3. Business software solution impact analysis skills YY Student Tasks to Achieve Outcomes Understand the business value of of data warehousing and business analytics, and how technology can be used to create this value Some of the examples and exercises will focus on business analytics for risk analysis Examples, exercises and assignments will draw from real problems in specific industries E.g., Banking/Financial Services, Retail/Hospitality/Entertainment, Telecommunications Faculty Methods to Achieve Outcomes (Assessment Methods will be developed in next phase of detailed course design) Grade Assignment 1, 2, mid term and final exam Grade Assignment 1, 2, Mid term and Final Exam Grade Assignment 1, 2, Mid term and Final Exam 2 IT architecture, design and development skills 1. System Requirements Specification skills 2. Software and IT architecture analysis and Design skills YY YY 3. Implementation skills YY 4. Technology Application skills YY Students will learn about key requirements for business analytics solutions Students will learn how to architect and design solutions using established building block applications and components Students will develop, configure and validate working solutions Students will do assignments, labs and projects that taken from the context of how business analytics are used in selected industry sectors and business functions Lab and Project Lab and Project Lab and Project Lab and Project 3 Project Management skills 1. Scope Management skills 2. Risks Management skills 3. Project Integration and Time Management skills 4. Configuration Management skills 5. Quality Management skills IS417 Data Warehousing and Business Analytics Page 8

9 4 Learning to Learn skills 1. Search skills YY 2. Skills for developing a methodology for learning YY Students are given problems where they will have to go beyond the materials and references given in class. They will have to systematically search to find more information that will be required to execute their assignments, labs and projects. Students are given opportunity to learn on their own when working on the assignments and class exercises Grade Assignment 1, 2, mid term and final exam Grade Assignment 1, 2, mid term and final exam 5 Collaboration (or Team) skills: 1. Skills to improve the effectiveness of group processes and work products 6 Change management skills for enterprise systems 1. Skills to diagnose business changes 2. Skills to implement and sustain business changes 7 Skills for working across countries, cultures and borders 1. Cross-national Awareness skills 2. Business across Countries Facilitation skills Y Includes how to distribute the business analytic results throughout a globally distributed enterprise 8 Communication skills 1. Presentation skills YY 2. Writing skills YY Students will present their solutions and results, will have their presentations critiqued. Students will also submit written summaries of their assignments, labs and projects. Grade Project presentation Grade Project report, assignment, mid term and final exam Y : This sub-skill is covered partially by the course YY : This sub-skill is a main focus for this course IS417 Data Warehousing and Business Analytics Page 9

10 6. Classroom Planning Each week there will be three hours of lectures during which theory, practical demonstrations and case-studies will be presented. Each class is split into two sessions of 1.5 hours. In general, the first session is used for lectures, while the second session is for labs, tutorial and in class discussions. However, there may be variations from week to week as appropriate. SAS BI toolkit, Oracle BI tools and retail examples are used for Labs and case studies in this course. In addition, weekly consultation session is available to solve student questions or enquiries about the course during teaching session. Before final examination, extra consultation time will be allocated and detail will be announced in class. All important announcements will be posted to the Course Vista Notice board. Urgent announcements will also be mailed to all members of the class. To enhance the content of course, guest speakers from industry will be invited to give a talk for introducing the state of the art in application domains of BI and BA. 7. Course Schedule Summary Week Date Topic Assignment & Project 1 Course Overview Part I: Basic Knowledge about Data Warehousing 1 Overview of Data Warehousing & Business Intelligence SAS Introduction Warm Up Lab for SAS and SAS BI package 2 Dimensional Modeling I: Basics Why we need data modeling Review on E-R model Data Modeling for DW: Fact, Dimension, Snowflake and Star Schema Four steps for dimensional model design Invited Talk I: SAS BI and DW package (60 mins) (To be confirmed) 3 Dimensional Modeling II: Advanced Topics Case Study for Dimensional Modeling Retail Case Study Schemes for dynamic changing (slowly and fast) Large dimensions Tutorial 1 Lab 1: Dimensional Modeling IS417 Data Warehousing and Business Analytics Page 10

11 4 Extract, Transform, Load (ETL) PS 1 out Data quality Basic process for ETL DW tem architecture for ETL Tutorial 2 Lab 2: External Data and ETL Part II: Data Warehousing & OLAP 5 OLAP and Data Cube Proj. Doc Basic components for OLAP and Data Cube Basic data analysis using Data Cube Tutorial 3 Lab 3: OLAP, Data Cube and Data Analysis I 6 Data Warehouse Architecture, Development and Management Review of different data warehouse architectures Review of different development methodologies Lab 4: OLAP, Data Cube and Data Analysis II 7 1. Decision Support System 2. Fundamentals of Business Intelligence 3. Data Warehouse and BI Tutorial 4 8 Session break Part III: Data Warehousing and Business Intelligence 9 Mid Term Examination Lab 5: Dashboard and Reporting Functionality PS 2 out PS 1 Due 10 Business Intelligence Applications Types of BI applications Navigating Applications via the BI portal Tutorial 5 11 Data Warehouse, WWW and ebusiness Exploring User Generated Content, Sentiment Analysis Invited Talk II: Business Intelligence Applications in real world (To be confirmed) IS417 Data Warehousing and Business Analytics Page 11

12 Lab 6: Large scale information analysis and mining for BI 12 Team project presentation I PS 2 Due 13 Team project presentations II Due: Team Project Report 14 Student study 8. List of Information Resources and References Lecture notes are the primary source that students used for reviewing the class content. They are preprints of PowerPoint slides used by instructor during lecture delivery and can be downloaded from the portal before the class each week. At the same time, student can gain knowledge by accessing resources mentioned in the following sections. 7.1 Core Text Books: [B01] The Data Warehouse Lifecycle Toolkit: Practical Techniques for Building Data Warehousing and Business Intelligence Systems, R. Kimball et al., 2 nd Edition, Wiley, [B02] Decision Support and Data Warehouse Systems, E. G. Mallach, McGraw-Hill, [B03] Data Mining: Concepts and Techniques (Second Edition), J. Han and M. Kamber, Kaufmann Publishers, Reference Books: [B04] Fundamentals of Data Warehousing (Second Edition), M. Jarke et al. Springer Verlag, [B05] Database Management Systems (Third Edition), R. Ramakrishnan and J. Gehrke, McGraw-Hill, 2003 Note: Both core text books and reference books are available on reserve at SMU library. 7.3 Reference Papers and Web Links [RP01] J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, H. Pirahesh: Data Cube: A Relational Aggregation Operator Generalizing Groupby, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery. 1(1): (1997) [RP02] S. Chaudhuri, U. Dayal: An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1): (1997) [RP03] Zaima and Kashner, A Data Mining Primer for the Data Warehouse Professional Business Intelligence Journal, pp , Spring (2003) [RP04] H. Watson and T. Ariyachandra, "Benchmarks for BI and Data Warehousing Success," DM Review, January (2006) IS417 Data Warehousing and Business Analytics Page 12

13 7.4 Other Course Materials Reading packet and cases available via SMU vista Additional video clips and hand out in class 7.5 Reference Web Links [RW01] Kimball group - [RW02] Business object - [RW03] Oracle BI - [RW04] SAS BI - [RW05] Oracle Data Warehorse for Retrail, 10g release 2, tutuorial, examples and Quiz. 9. Tooling SAS BI Software Package Oracle Data warehousing platform, OLAP and BI Analysis Software Package Oracle retail BI analysis example and tutorial 10. Weekly Plan Week: 1 Session 1: Overview of Data Warehousing & Business Intelligence Session 2: Warm-up lab session & SAS introduction Reading: [B02] Ch12 [B04] Ch 1 Week: 2 Session 1: Dimensional Modeling I: Basics Session 2: An invited talk given by speaker from SAS (To be confirmed) Reading: [B03] Ch 2.2 [B01] Ch 5 (excluding pp and ) IS417 Data Warehousing and Business Analytics Page 13

14 Week: 3 Session 1: Dimensional Modelling II: Advanced Topics Case Study for Dimensional Modeling Retail Case Study Schemes for dynamic changing (slowly and fast) Large dimensions Session 2: Tutorial 1 Lab: In-Class Lab 1 Reading: [B01] Ch 5 (pp and ) [R01] Week: 4 Session 1: Extract, Transform, Load(ETL), Performance Tuning Session 2: Tutorial 2 Lab: Lab 2 Reading: [B01] Ch 16 Week: 5 Session 1: OLAP and Data Cube Basic components for OLAP and Data Cube Basic data analysis using Data Cube Session 2: Tutorial 3 Lab: Lab 3 - OLAP, Data Cube and Data Analysis I Reading: [B01] Ch 18 [B03] Ch 2.3 and 2.4 Week: 6 Data Warehouse Architecture, Development and Management Review of different data warehouse architectures Review of different development methodologies Lab: Lab 4 - OLAP, Data Cube and Data Analysis II Reading: [B03] Ch 2.6 [B01] Ch 1 [B05] Ch 25.2 and 25.8 Project: Project Description Release IS417 Data Warehousing and Business Analytics Page 14

15 Week: 7 Section 1: 1. Decision Support System 2. Fundamentals of Business Intelligence 3. Data Warehouse and BI Section 2: Tutorial 4 Reading: [B03] Ch 2.6 [B02] Ch14.2 Week 8: Recess Week: 9 Mid-term examination Lab 5: Dashboard and Reporting Functionality Week: 10 Session 1: Business Intelligence Applications Types of BI applications Navigating Applications via the BI portal Session 2: Tutorial 5 Lab: Lab 6 -Large scale information analysis and mining for BI Reading: [R04] Case Document Week: 11 Session 1: Data Warehouse, WWW and ebusiness Exploring User Generated Content, Sentiment Analysis Session 2: Invited Talk II: Business Intelligence Applications in real world (To be confirmed) Reading: Handout in class IS417 Data Warehousing and Business Analytics Page 15

16 Week: 12 Session 1: Student project presentation Session 2: Discussion Week: 13 Session 1: Student Presentation Session 2: Course Review Assignment: Student feedback Peer assessment Reading: Handout Project: Project report due and students need to hand in their report before class presentation. Week 14: Study Week Week 15: Final Exam IS417 Data Warehousing and Business Analytics Page 16