Course Syllabi
Course Symbol: CPSC 5230
Title: Business Intelligence Programming and Analysis
Hours of credit: 3
Catalog Description:
Provide a comprehensive discussion on advanced database system, data warehousing, online analytical processing, data mining, decision support systems, artificial intelligence and other Business Intelligence topics.
Course Description:
This course provides a comprehensive discussion of and practical experience in advanced database techniques, data visualization, data warehousing, online analytical processing (OLAP), data mining, decision support systems (DSS), artificial intelligence (AI) methods and other Business Intelligence (BI) topics. Students gain practical experience using contemporary BI tools and technologies, and apply sound design principles for creating intelligent solutions to realistic business problems. Prerequisite: CPSC5000 or approval of department head
Overview:
Organizations rely on computer-based information system for capturing, analyzing, and distributing the information required to develop, implement, and evaluate corporate strategies in all functional areas. Managing data as a corporate resource requires a deep understanding of business processes and of the underlying structure of the data needed to support them. Business intelligence refers to the process of capturing and storing data to be transformed into useful information to support managerial decision-making. The focus of this course is on concepts central to the management of data resources and the development of business intelligence capabilities. There is a mixture of theoretical and practical topics including case studies and a significant hands-on component.
Textbooks:
IBM Cognos Business Intelligence V10.1 Handbook.
Delivering Business Intelligence with Microsoft SQL Server 2008, by Brian Larson, ISBN 978-0-07-154944-8, McGraw Hill, 2009
Decision Support and Business Intelligence Systems, 9th Edition, Efraim Turban, University of Hawaii, Jay E. Aronson, University of Georgia, Ting-Peng Liang, National Sun Yat-Sen University, Ramesh Sharda, Oklahoma State University, ISBN-10: 0131986600, ISBN-13: 9780131986602, Publisher: Prentice Hall, Copyright: 2007
Business Intelligence: A Managerial Approach 2/e by E. Turban, R. Sharda, D. Delen, D. King. Pearson Prentice Hall, Inc. 2010. ISBN-13: 978-0-13-610066-9. ISBN-10: 0-13-610066-X.
COURSE REQUIREMENTS:
1. Regular class attendance is the main requirement of this course.
2. Active class participation, this means you must spend some quality time preparing for your next class.
3. Programming assignments, homework, and reports of hands-on labs must be turned in on time when they are due. Unfinished programs and non-working programs turned in on time will be graded; however, assignments not turned in on the due date will NOT be accepted. This means that you should start early to work on your programming assignments. Programs must be well-documented to be understood by a novice programmer.
4. Short quizzes may be given without prior notice and there will be no making up of missed quizzes.
5. Two examinations and a final examination will be given. There will be NO make up for missed exams.
6. You will be issued with one computer account for this class. You have a responsibility and an obligation to prevent abuse and misuse of the university computer resources. Please read the UTC Computer Use Code of Conduct.
7. Individual extra credit assignments for the purpose of propping up a bad grade will NOT be given.
Notes taking is encouraged. Notes for the class can be found at the bb4.utc.edu. You can also check the website of textbook for more resources.
Course Outline
Week 1 Intro to Decision Support Systems and BI, Review of relational database and SQL
Week 2 Decision Making, Systems, Modeling, and Support, Advanced SQL queries
Week 3 Decision Support Systems Concepts, Methodologies, and Technologies: An Overview, Triggers and Stored Procedures, Atomic Transactions
Week 4 Modeling and Analysis
Week 5 An introduction/review of transaction processing systems
Week 6 Data Mining for Business Intelligence
Week 7 Data Warehouse scheme: Operational and Star Schemas, Pivot Tables and Charts, Executive Dashboards
Week 8 Data Warehousing (ETL) and concept of multi-dimension cubes
Week 9 Online Analytical Processing (OLAP)
Week 10 OLAP, Microsoft SSAS Tutorials, Microsoft MDX
Week 11 Artificial Neural Networks for Data Mining, IBM Cognos
Week 12 Concept of data and knowledge management
Week 13 Artificial Neural Networks for Data Mining
Week 14 Data Mining and Text/Web mining
Method of Evaluation:
90+ = A; 80-89 = B; 70-79 = C; 60-69 = D; below 60 = F.
Assignments 30%
Graduate Project 15%
Exams (1 & 2) 30%
Final 25%
Total 100%
Justification:
This course is proposed to meet a growing business need of individuals skilled in information and business intelligence, data analytics, business programming and other software skills. The proposed course will combine theory and practice to enable the student to gain the necessary knowledge to compete in the ever changing work environment. Students will learn concepts and methods designed to improve the business decision-making process by putting targeted information into the hands of those who need it most. They will understand business critical processes that drive organizational structures and systems within the context of varying stakeholder interests. Additionally, they will be able to define and evaluate potential initiatives that best fit organizational goals. This proposal will enable UTC to meet the need of local industry such as Blue Cross Blue Shield, U.S. Xpress, UNUM, etc., and educate professionals in areas of business intelligence and data analytics. Specifically, at the end of this course students should be able to effectively develop, manage, integrate, and use corporate information resources. Specifically, students should be able to:
1. provide a working definition for business intelligence in general and various classifications of business intelligence.
2. build upon and enhance his knowledge of relational database technology and skills performing complex database queries, triggers, and stored procedures.
3. become familiar with a variety of data visualization options, including bar/line/pie/bubble charts, digital dashboards, virtual reality displays, and key performance indicator gauges.
4. apply data visualization techniques to a wide variety of data sources in order to present user-friendly and informative interfaces to end-users.
5. demonstrate knowledge of the processes used to extract operational data, transform and cleanse this data, and load it into a data warehouse or data mart.
6. demonstrate a working knowledge of the difference in structure between relational databases and multidimensional data warehouse architectures.
7. demonstrate a working knowledge of relationship between facts tables and dimension tables, as well as understanding basic star and snowflake schemas.
8. design online analytical processing (OLAP) models, and build multidimensional cubes that are capable of providing summary information as well as drilling down for detailed data.
9. demonstrate a working knowledge of a variety of data mining models and structures: inductive decision trees, naïve Bayes algorithms, clustering algorithms, neural networks, and time sequences.
10. apply data mining models to real-world data sets to train the models for predictive behavior, and then apply the trained models to test data in order to evaluate their accuracy and reliability.
11. learn how to enhance data mining performance by modifying model parameters and adjusting feature selection decisions.
12. use commercial and open-source business intelligence tools to develop their BI applications.
Evidence of Post-Baccalaureate Rigor
Graduate Students will be challenged with extensive reading, writing and graduate project with research value. They are required to undertake and successfully finish a semester-long graduate project. Students select the topics of interest on the condition that they have research components and are related to the course. Potential topics and resources are included in the graduate project section of each attached syllabus. Projects, based on students’ interests, will be approved by instructors of the course. Students can search the ACM Digital Library or IEEE Explorer in the UTC on-line library. Students need to provide a page of abstract, a project report, and a PowerPoint version of slides for the presentation. All the presentations must address the following questions:
How is the problem to be solved?
What is the author’s solution(s)?
How are the solutions to be evaluated?
What are the strengths, compared with prior works?
Do you think there is any weakness in the proposed work?
Resources
This proposal does not require any additional resources such as staff support, financial resources or physical facilities from the Department of Computer Science and Engineering or any other departments or programs. This course will allow us to more fully utilize the resources we have already in the department. We have the capacity to enroll more graduate students than we presently have, and this concentration will help us recruit more graduate students and utilize our capacity.
Contact
Li Yang,
Planned Frequency
Fall semesters
Explanation of Duplication
There is NO duplication or overlapping of proposed course content with courses offering from other departments.
ADA STATEMENT: Attention: If you are a student with a disability (e.g. physical, learning, psychiatric, vision, hearing, etc.) and think that you might need special assistance or a special accommodation in this class or any other class, call the Office for Students with Disabilities at 425-4006, come by the office - 102 Frist Hall or see http://www.utc.edu/OSD/
If you find that personal problems, career indecision, study and time management difficulties, etc. are adversely affecting your successful progress at UTC, please contact the Counseling and Career Planning Center at 425-4438 or http://www.utc.edu/Administration/CounselingAndCareerPlanning/.
USEFUL RESOURCES
Decision Support Systems: A Knowledge-Based Approach (Online textbook by Clyde W. Holsapple)
DSS Resources Website by Daniel Power
DSS-Software by dssresources.com
DSS-Software by J.E. Aronson
DSS-Software by Vicki L. Sauter
DM Review - Covering Business Intelligence, Integration and Analytics. Excellent resource for white papers on BI, data warehousing, data mining, CRM, analytics, integration and content management news
Balanced Scorecard Institute - training, consulting and guidance to assist government agencies and companies in applying best practices in balanced scorecard (BSC) and performance measurement for strategic management and transformation
Data Warehousing - Sponsored by DM Review, dedicated to data warehousing.
IT Toolbox - Collaborative network for the IT market, providing actionable IT content.
SAS - Link to SAS homepage
Microsoft SQL Server BI - Link to Microsoft Business Intelligence information site
Business Intelligence Toolbox - Sponsored by IT Toolbox, dedicated to BI.
InfoWorld - Delivers in-depth coverage and evaluation of IT products for technology experts making major purchasing decisions. Good articles, reviews, etc.
The OLAP Report - Outstanding web resource for On-Line Analytical Processing software, knowledge, and industry information
Exploding OLAP Databases - Links to exploding OLAP databases by OLAP Report, with images and explanations
DSS Resources - Knowledge repository about computerized systems that support decision making.
Wherefore Warehouse - A glimpse of the past and future of data warehouses, data marts and data mining
Kurt Thearling - Data Mining - Nice overview of data mining, with tutorial and white papers
Advizor Visual Discovery - Corporate site with case studies for companies using data visualization to solve problems and improve business
Building a Data Warehouse on the Web - 1998 AIR Forum Demonstration by Milam and Wood, George Mason University
Business Intelligence Applications
Google - Search Engine
Awesome Data Visualizations - Developed in Germany by Deutsches Klimarechenzentrum (DKRZ)
Google Earth - Look for your house! An application of layered data visualization
Google Mars - Look for your in-law's house!
Craig's List - A growing reservoir of multi-linked, multi-dimensional and often visual knowledge management. In short, a fascinating application of BI!
Powers of 10 - A fascinating demonstration of multidimensional scaling, compliments of FSU
Wikipedia - User-supported website with phenomenal breadth and depth of knowledge on just about any subject (not always refereed - use at your own risk!)
Excel User BI Tools - BI tools built in Excel, with free demo downloads (may require registering with provider). Be sure to explore the root website
Jon Peltier's Excel Charts - Outstanding resource for making data sing with Excel graphs, from Peltier Technical Services
Vertex^42 - A collection of downloadable Excel templates and articles on Excel applications, such as building dashboards
Statistics Links
HyperStat Online - David M. Lane's on-line statistics textbook. Worth book marking!
Keith Bower - ASQ member with a wonderful website devoted to articles and information on statistics. Keith has an MS in Statistics from Iowa.
Wikipedia Statistics Package - From Wikipedia, has nice discussions and examples on statistics concepts, information on software packages, etc.
Stat Crunch - (Requires account) - Statistical software for data analysis on the web
Seeing Statistics - Teaching statistics on the web. Nicely done!
Interactive Demos of Stats Concepts Using JMP - Dynamic illustrations, all require JMP software.
Rice Virtual Lab of Statistics - Variety of Java-based dynamic illustrations.
Statistical Applets - Collection of dynamic illustrations from a lousy basketball school (Duke University)
Probability by Surprise - Java applets visualizing probabilities.
Type I and Type II Errors - Java based illustration of the judicial system.
Statistics Every Writer Should Know - by Robert Niles (light, but funny site, and useful, too!)
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