Meetings This class meets on Mondays from 6:20 PM to 9:05 PM in CIS Room 1034 (in class delivery of instruction).

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1 Clinton Daniel, Visiting Instructor Information Systems & Decision Sciences College of Business Administration University of South Florida 4202 E. Fowler Avenue, CIS1040 Tampa, Florida Office Hours: CIS2070A from 5:00 PM -6:00 PM Mondays Tel: (813) Fax: (813) ISM4219 Business Intelligence (3 Credit hours) Syllabus Fall 2013 Course Materials Required Text: Mundy, Thornwaite, and Kimball, The Microsoft Data Warehouse Toolkit, Second Edition, John Wiley & Sons, Outside readings will be used to supplement the text. You should also have access to a current database textbook and Microsoft database documentation since this technology suite will be used to implement the semester projects. There are also plenty of manuals and tutorials available on the course website. The Microsoft SQL Server tools are available from the ISDS department Microsoft Academic Alliance (as well as Microsoft s DreamSpark website). We will be using the USF Canvas portal at my.usf.edu, so make sure that you have a current login. This website offers lecture presentations, online reference materials, additional readings, and links to other resources. Prerequisites ISM 4212 with C grade or higher is required prior to taking this course. Meetings This class meets on Mondays from 6:20 PM to 9:05 PM in CIS Room 1034 (in class delivery of instruction). Course Description For undergraduate information systems students, as well as other interested business students. The course covers the rapidly emerging business intelligence and data mining technologies that are likely to play a strategic role in business organizations. Objectives This course is designed for undergraduate information systems students, as well as other interested business students. The course covers the rapidly emerging business intelligence and data mining technologies that are likely to play a strategic role in business organizations. A series of independent projects will be used to gain hands-on experience 1

2 with the core topics, such as Transact-SQL, Data Warehouse design, BI Interface design, and Data Mining. The projects are designed to be self-contained so that each provides a fresh start. Students should gain some experience in understanding database execution plans, as well as query writing and tuning. At least one of the projects will include analytic query writing and the use of online analytic processing (OLAP) tools. The final project will focus on data mining and involve understanding the underlying target data sets, selection of data mining technologies, and the implementation of preliminary predictive models. Learning Outcomes Upon completion of this course students should know differences between operational and analytical database systems, dimensional modeling (data cubes) and star schemas, data warehousing, online analytic processing (OLAP) tools, selected data mining techniques, and knowledge of BI management issues. Example business issues include customer retention models, purchasing models, and click stream analysis for understanding online consumer behaviors. Students will learn how to use Microsoft SQL Server database, along with Analysis Services, to illustrate many of the concepts covered in class, and provide a platform for hands-on projects. Course Grading Policies Course grades will be based on laboratory write-ups, examinations, and assignment projects. The (+/-) grading system will be used for final grades. The tentative weights for the various examinations and projects are as follows. Examinations (Online Quizzes): 30% Assignment Projects: 60% Class Participation: 10% A series of independent projects will be used to gain hands-on experience with the core topics, such as Transact-SQL, Data Warehouse design, BI Interface design, and Data Mining. The projects are designed to be self-contained so that each provides a fresh start. Students should gain some experience in understanding database execution plans, as well as query writing and tuning. At least one of the projects will include analytic query writing and the use of online analytic processing (OLAP) tools. The final project will focus on data mining and involve understanding the underlying target data sets, selection of data mining technologies, and the implementation of preliminary predictive models. Attendance Policy: It is assumed that students will make an effort to attend all classes. If a student is unable to attend a specific class, they should review any relevant online materials. If an assignment is missed, the students must make arrangements with the instructor to complete the required work. For any make-up examinations, both a written assessment and interview will be used as evaluations. Course Topics The course topics are divided into seven major parts, including hands-on coverage of data warehouse design and implementation techniques. 2

3 Part I: Business Intelligence Overview The first meetings provide an overview of business intelligence issues, along with data warehousing and data mining technologies. The differences between operational and analytic databases are highlighted, along with matters of scale (data marts and data warehouses). Important presentation technologies, including online analytic processing (OLAP) tools, geographic information systems and data mining techniques are briefly introduced. Part II: Data Warehousing and Online Analytic Processing (OLAP) Key data warehouse design techniques involving dimensional modeling and star schemas, as well as the role of metadata, are studied in detail. Analytic queries and the use of online analytic processing (OLAP) tools are reviewed. An example data warehouse will be used to introduce the concepts. Part III: Data Mining The focus of this section is selected data mining techniques, including decision tree induction, neural networks, market basket analysis, and clustering. Each of the techniques 3 will be covered through both lecture and laboratory approaches. Part IV: Data Visualization Data visualization techniques such as geographic information systems, visualization toolkits, and other presentation features often used in conjunction with OLAP interfaces are considered. Again, hands-on examples will be used to illustrate the concepts. Part V: Knowledge Management The final module will focus on reviewing the more general knowledge management challenges faced by organizations. Topics include managing corporate knowledge assets, expertise locators, and other human resource efforts, as well as text mining and document management. General University Policies Policy on Religious Holidays: Students who anticipate the necessity of being absent from class due to the observation of a major religious observance must provide notice of the date(s) to the instructor, in writing, by the second class meeting. Policy on University Closures: In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Canvas, Elluminate, Skype, and messaging and/or an alternate schedule. It is the responsibility of the student to monitor the Canvas site for each class for course-specific communications, and the main USF, College, and department websites, s, and MoBull messages for important general instructions. Please note that USF regulations state that only students who are registered for this class may attend and be present in the classroom while presentations are being made. Any students who feel they need assistance to attend class must contact USF s Office of Student Disability 3

4 Services ( ) and request a review and authorization if appropriate for necessary support. Academic Integrity of Students: Disruption of the Academic Process: Student Academic Grievance Procedures: Students with Disabilities: Students with disabilities are responsible for registering with Students with Disabilities Services (SDS) in order to receive academic accommodations. SDS encourages students to notify instructors of accommodation needs at least 5 business days prior to needing the accommodation. A letter from SDS must accompany this request. See student responsibilities: See instructor responsibilities: SafeAssign Privacy policy: In order to comply with privacy laws, students are not required to include personal identifying information, such as your name, in the body of the document. Submitting to the SafeAssign Global Reference Database allows papers from other institutions to be checked against your paper to protect the originality of your work across institutions. Please follow your instructor's instructions carefully regarding what identifying information to include. Blackboard Quick Reference Guide - Submitting SafeAssignments University Emergency Policy: In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and messaging and/or an alternate schedule. It's the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, s, and MoBull messages for important general information. 4

5 Fall 2013 Calendar (Tentative 16 Week) August Week 1: 8/26 Business Intelligence Overview Topics include an overview of BI technologies, contrasting operational and analytic databases, as well as general data warehouse architecture issues. Readings: MDWT Introduction, Chapter 1 and online materials. Quiz #1 The Kimball Lifecycle (Login to Canvas for details): Quiz #1 is DUE on 9/9. September Week 2: 9/2 Labor Day (No Class) Week 3: 9/9 Fundamentals of SQL This module will review the fundamentals of Standard SQL and Microsoft SQL Server T-SQL. Readings: Online materials. Assignment #1 (Login to Canvas for details): T-SQL. Assignment #1 is DUE on 9/23 Quiz #1 is DUE Quiz #2 BI Overview from Online document (Login to Canvas for details). Quiz #2 is DUE on 9/16. Week 4: 9/16 Fundamentals of SQL This module will continue the review the fundamentals of Standard SQL and Microsoft SQL Server T-SQL. Readings: Online materials. MDWT Chapter 2 Quiz #2 is DUE Week 5: 9/23 Designing the Data Warehouse Key design techniques involving dimensional modeling and star schemas will be introduced. Readings: MDWT Chapters 5, 6, and 7 (core reading). Assignment #1 is DUE (Upload assignment to Canvas) Assignment #2 (Login to Canvas for details): Data Warehouse Design. Assignment #2 is DUE on 10/7. Quiz #3 Creating the Relational Data Warehouse (Login to Canvas for Details). Quiz #3 is DUE on 9/30. October Week 6: 9/30 Designing the Data Warehouse Discussion of design techniques will continue Readings: MDWT Chapter 5, 6, and 7. 5

6 Quiz #3 is DUE Week 7: 10/7 Using the Data Warehouse Important interface design issues will be discussed, including coverage of Reporting Services, Excel Services, PowerPivot Services, and SharePoint Services. Readings: MDWT Chapters 8, 10, 11, and 12 (core reading). Online materials. Assignment #2 is DUE Quiz #4 Includes content from Chapters 8, 10, 11, and 12 (Logon to Canvas for Details). Quiz #4 is DUE on 10/21. Week 8: 10/14 Using the Data Warehouse The discussion of various interface designs will continue. Readings: MDWT Chapters 8, 10, 11, and 12. Online materials. Assignment #3 (Login to Canvas for details): BI Interface Design Project. Assignment #3 is DUE on 10/28. Week 9: 10/21 Using the Data Warehouse The discussion of various interface designs will continue. Readings: MDWT Chapters 8, 10, 11, and 12. Online materials. Quiz #4 is DUE Week 10: 10/28 Designing and Implementing Security Coverage of Business Intelligence and Data Warehouse Security Readings: MDWT Chapter 14. Assignment #3 is DUE November Week 11: 11/4 Administering the Data Warehouse Capacity planning and performance issues will be considered, including indexing strategies and query optimization. Database Administration. Readings: Online Materials. Week 12: 11/11 Veteran s Day (No Class) Week 13: 11/18 Managing the Data and the Data Warehouse Overview of the Metadata plan. Deploying and Managing the Data Warehouse/Business Intelligence System. Operations and Maintenance. Issues involving project justification, staffing requirements and the data warehouse development lifecycle will be discussed. Readings: MDWT Chapters 15, 16, and 17. Quiz #5 Includes Content from Chapters 15, 16, and 17. Quiz #5 is DUE on 11/25. 6

7 Week 14: 11/25 Mining the Data Warehouse Topics will include knowledge discovery in databases, widely used data mining techniques, and visual presentations. Readings: MDWT Chapter 10. Quiz # 5 is DUE Assignment #4 (Login to Canvas for details): Data Mining Project. Assignment #4 is DUE on 12/9. December Week 15: 12/2 Mining the Data Warehouse Readings: MDWT Chapter 10. Data mining coverage will continue. Week 16: 12/8-14 Final Exam Week Assignment #4 is DUE on 12/9. The readings are from the recommended text by Kimball et al., The Microsoft Data Warehouse Toolkit (MDWT), as well as online bookmarked materials. 7