ClassInfo - Spring 2016 Information and Decision Sci Classes

Spring 2016 | IDSC 4431 Section 001: Advanced Database Design (55348)

Instructor(s): Gordon Everest
Class Component:: Lecture
Credits:: 2 Credits
Grading Basis:: A-F only
Instructor Consent:: No Special Consent Required
Instruction Mode:: Primarily Online
Times and Locations:: Regular Academic Session

01/19/2016 - 05/06/2016

Thu 04:30PM - 06:10PM

UMTC, West Bank

Carlson School of Management 1-114

Course Catalog Description:: Reviews ER/relational modeling and normalization, then focuses on fact modeling (ORM) to produce advanced richer business data models. "Flipped" class, fully online, including all lectures & final exam. Weekly in-class review session is recorded and online for questions, discussion, and results of assignments & quizzes. prereq: 3103 or CSCI 4707 or CSCI 5707 or instr consent
Class Notes:: Please see URL: http://GEverest.umn.edu/home/data-modeling-course For more information you may contact: geverest@umn.edu
Class Description:: Raise your database design skills to a whole new level. This course receives very high ratings from students. One wrote "enjoyed your class tremendously; one of the best I had at the U." Students learn something substantive to greatly enhance a career in IS, and have fun in the process. This course presents a high-level, conceptual data modeling scheme called Object Role Modeling (ORM). Students gain practical experience developing data models using ORM and its supporting CASE tool, the Natural ORM Architect (NORMA), which is open source software. Topics include: the basics of Entity-Relationship Diagrams (ERD), relational database modeling, and the principles of data normalization; a comprehensive taxonomy and comparison of data modeling schemes; advanced constructs in database design - subtypes and supertypes, ternary relationships, integrity constraints; capturing and representing facts and advanced business rule semantics in a data model; presentation and verbalization of a data model diagram for human understanding and validation; conducting a data modeling project; and mapping an ORM data model to a relational data model. ORM is a new way of thinking which avoids "table think." It enables data modelers to produce models which are semantically richer and easier to understand than traditional ?record-based? data modeling schemes, such as Entity-Relationship (ER) Diagrams and relational tables. TEXT: Terry Halpin and Tony Morgan, Information Modeling and Relational Databases, Morgan-Kaufmann, 2008. AUDIENCE -- For the student or practicing IS professional who wants to learn how to do a good job of designing databases. This course provides the necessary methodology and tool skills. .. Lecture with much interactive discussion. Lectures, and quiz and assignment reviews are recorded and available on the web. A special review session on Mondays starting at 4:30 pm for students who missed a live class lecture, want to ask questions, or dig more deeply into a topic. .. For more information, go to: http://www.tc.umn.edu/~geverest/DModCourse.html
Grading:: 20% Final Exam
20% Quizzes
50% Written Homework
10% Class Participation Other Grading Information: A 15 min, 2 page quiz each week an assignment is not due plus a final exam. All are taken online.
Exam Format:: Both quizzes and the final exam: a mix of TF, MC, Blank fill, short answer, and mini design problems. Final exam is comprehensive on the whole course.
Class Format:: 70% Lecture
20% Discussion
10% Demonstration Lecture with much interactive discussion. Lectures, and quiz and assignment reviews are recorded and available on the web. An optional review session on Mondays starting at 4:30 pm.
Workload:: 25 Pages Reading Per Week
1 Exam(s)
5 Homework Assignment(s)
5 Quiz(zes)
Other Workload: Within University guidelines of 2+ hours per week for every credit hour spent in class. Students report an average of about 25 hours total on the assignments.
Textbooks:: https://bookstores.umn.edu/course-lookup/55348/1163
Instructor Supplied Information Last Updated:: 10 November 2012

ClassInfo Links - Spring 2016 Information and Decision Sci Classes

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