IS 574 Business Intelligence and Analytics Systems
Summary
This course provides an introduction to 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.
Texts
No assigned textbook is used. Students are required to purchase one Harvard Business School case from http://cb.hbsp.harvard.edu/cbmp/access/78118885 (with a discount price):
1) Caterpillar Tunneling: Revitalizing User Adoption of Business Intelligence Frances Leung; Murat Kristal
2) 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?
1) Business Intelligence Guidebook ? From Data Integration to Analytics by Rick Sherman
2) Business Intelligence: The Savvy Manager?s Guide by David Loshin
3) AI in the 21st Century (2nd edition) by S Lucci and D Kopec
4) Practical Text Mining by Gary Miner
Grading
Grading
? Assignments 30%
? Case Studies 25%
? Term Project
? Part I 20%
? Part II 20%
? Class Participation 5%
Prerequisites
(SE 430 or IS 435 or PM 430 or MIS 674) and CS C451
Course Overview and BI Overview
BI Basics
Information gathering and Decision-making
Managing BI
BI User Segmentation
Term Project - Part I - In Class Presentations
Gathering BI Requirements
Big Data
AI and Machine Learning
Term Project - Part II - In Class Presentations
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.
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Students complete the evaluation online in CampusConnect.
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have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.
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