Same school, new name. The School of Informatics and Computing is changing its name effective January 11, 2023. Learn more about the name change

LIS-S 511 Database Design

3 credits

  • Prerequisites: LIS-S 500, LIS-S 507
  • Delivery: On-Campus, Online
  • Semesters offered: Fall, Spring
    The above are the semesters this course is generally offered. View the course schedule to confirm.
  • Note: S503 prerequisite not necessary for Digital Curation Specialization.

    Concerned with a comprehensive view of the processes involved in developing formal access to information from a user-centered point of view. Considers various database models such as flat file, hierarchical, relational, and hypertext in terms of text, sound, numeric, image, and geographic data. Students will design and implement databases using several commercial database management systems.

    Online or on campus?

    The online version of this course is tailored to the needs of students in the Master of Library and Information Science program. It is recommended that MLIS students take the online version in the spring semester.

    The on-campus version is tailored to the needs of students in the Master of Science in Applied Data Science program.  Students who have completed an upper-level undergraduate or graduate database course within the past two years with a grade of B– or higher should instead take CSCI 54100 Database Systems.

    Learning Outcomes

    1. Design and implement relational databases using tables, keys, relationships, and SQL commands to meet user and operational needs.
    2. Diagram a relational database design with entity–relationship diagrams (ERDs) using crow’s foot notation to enforce referential integrity.
    3. Evaluate tables for compliance to third normal form and perform normalization procedures on noncompliant tables.
    4. Write triggers to handle events and enforce business rules and create views within a relational database.
    5. Formulate queries in relational algebra using selection, projection, restriction, Cartesian product, join, and set operators.
    6. Demonstrate an understanding of the data lifecycle, including data curation, stewardship, preservation, and security.
    7. Evaluate the social and ethical implications of data management.