IS 574 Business Intelligence and Analytics Systems
This course introduces the concepts of business intelligence (BI) as components and functionality of information systems. It explores how business problems can be solved effectively by using operational data to create data warehouses, and then applying data mining tools and analytics to gain new insights into organizational operations. Detailed discussion of the analysis, design and implementation of systems for BI, including: the differences between types of reporting and analytics, enterprise data warehousing, data management systems, decision support systems, knowledge management systems, big data and data/text mining. Case studies are used to explore the use of application software, tools, success and limitations of BI as well as technical, managerial and social issues.
No assigned textbook is used. Students are required to purchase one Harvard Business School case from https://hbsp.harvard.edu/import/813807 (at a discounted price):
- Caterpillar Tunneling: Revitalizing User Adoption of Business Intelligence Frances Leung; Murat Kristal
- Managing with Analytics at Procter & Gamble (613045-PDF-ENG) Thomas H. Davenport; Marco Iansiti; Alain Serels
All the other reading materials are provided online available via Books 24X7 through the DePaul Library: http://library.books24x7.com.ezproxy.depaul.edu/bookshelf.asp?
- Business Intelligence Guidebook – From Data Integration to Analytics by Rick Sherman ISBN-13: 978-0124114616 ISBN-10: 012411461X
- Practical Text Mining by Gary Miner
- Term Project – Part 1
- Term Project – Part 2
- Case Studies
- Class Participation
30% (75 points)
20% (50 points)
20% (50 points)
20% (50 points)
10% (25 points)
100% (250 points)
See current course catalog.
In addition to online lectures and supporting Power Point Presentations, participation in online discussions is required as part of class attendance for this course. These will be posted to D2L each week and will be due by the end of day the following Monday. For example, a discussion topic that opens on April 5th will close at 11:30 PM on April 12th. Discussion questions will be locked after the due date and no more posts/replies will be accepted. Posts and replies need to show depth of thought to get credit. One-word responses or short phrase responses will not earn credit. In general, to receive credit and show thought, posts will need to be a paragraph or more as a general guideline. In addition, just cutting and pasting content from other sources will not earn credit. The point is to share your thinking on a topic with the rest of the class. In general, participation in a specific discussion topic can earn up to 5 points.
All assignments are due on the due date in D2L. No credit can be earned when the solution has already been discussed in class or when an online discussion forum’s time window has ended.
Late submissions will be penalized unless prior arrangements have been made with the instructor.
You will lose 25% of the possible credit if less than 1 week late, 50% of the credit if 1-2 weeks late, and 100% of the credit if more than 2 weeks late.
Course Overview and BI Overview
Term Project - Part 1
Storytelling with Data
AI & Machine Learning
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course
content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The
evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely
separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation.
Students complete the evaluation online in CampusConnect.
This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you
have any questions be sure to consult with your professor.
All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that
you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002