V475 Database Management Systems

Fall 2026  |  Undergraduate Course  |  In-Person  | Section 5960

Instructor: Roger Morris	Email: morrisr@iu.edu
Office: SPEA 428	Office Hours: 9:30-10:30 MW and by appointment
Course Meeting: 2:20-3:35 p.m.	Location: PV 278

 

Course Description

This course introduces the principles and practices of database management with an emphasis on designing, building, querying, and using relational database systems to solve practical organizational problems.

Students will develop skills in database design, SQL, normalization, cloud data platforms, data preparation, visualization, and database application development through hands-on exercises, team activities, and an applied semester project.

Course Goal

The goal of this course is to develop students' ability to design, build, query, and use relational database systems to solve practical organizational problems.

Through active learning, hands-on database development, team problem-solving, and applied projects, students will learn how data requirements are translated into database designs and how those designs are implemented, queried, evaluated, and communicated.

Learning Objectives

By the end of the course, students should be able to:

1. Explain fundamental database concepts and distinguish relational database systems from other approaches to storing and managing data.
2. Analyze organizational data requirements by identifying stakeholders, business rules, entities, attributes, relationships, and information needs.
3. Develop and interpret entity-relationship diagrams and other conceptual database models.
4. Translate conceptual designs into logical relational schemas using appropriate tables, keys, relationships, and constraints.
5. Apply normalization principles through third normal form and explain important normalization decisions.
6. Construct SQL queries to retrieve, filter, sort, aggregate, join, and summarize relational data.
7. Design, build, populate, and use relational databases while maintaining appropriate data integrity.
8. Use database application features such as forms, reports, menus, queries, and automation to support organizational workflows.
9. Use and compare traditional and cloud-based database and data platforms.
10. Prepare database data for analysis and visualization.
11. Evaluate database solutions in terms of usability, data quality, security, platform choice, limitations, and organizational needs.
12. Communicate database designs and solutions effectively to technical and nontechnical audiences.
13. Use generative AI and other technical tools responsibly while demonstrating independent understanding.
14. Collaborate effectively on database-development tasks.

Prerequisites and Student Technology Expectations

Prerequisites for V475 include V261 and V369 in SPEA, K201 in Kelley, or comparable background in information technology or information systems.

Students are expected to have basic computer competency, including the ability to manage files and folders, download and upload files, install or access software, work with spreadsheets, and follow technical instructions.

Previous database or SQL experience is not required.

Because the course uses several database, cloud, and visualization platforms, students should be prepared to complete software setup, activate accounts, and become familiar with new technical environments.

Students who believe they need additional technology preparation should contact the instructor early in the semester.

Course Technology, Software, and Platforms

Students should expect to use several technologies during the semester. Each supports a different part of the database and data-management process.

Microsoft Access

Used for relational database construction, queries, forms, reports, menus, macros, and Final Project application development.

Microsoft SQL Server

Used for SQL practice and experience with a traditional enterprise relational database management system.

Google Cloud / Qwiklabs

Used for guided experience with cloud database and data-management environments.

Google BigQuery

Used to apply SQL concepts in a cloud analytics environment.

Databricks Free Edition

Used to provide additional experience with a modern cloud data platform and to demonstrate how SQL and database concepts transfer across systems.

Tableau Prep Builder

Used to prepare, clean, combine, and transform data.

Tableau Desktop

Used to create visualizations and dashboards from database data.

Lucidchart or Similar Diagramming Tools

May be used to create entity-relationship diagrams and related design documentation.

Generative AI Tools

Tools such as ChatGPT, Microsoft Copilot, and similar systems may be used when permitted under the course AI policy.

Specific setup instructions will be provided in Canvas. Because software and cloud services change over time, the instructor may adjust the specific platforms or labs used while maintaining the same course learning objectives.

Course Requirements and Learning Activities

This course emphasizes active learning and applied practice. Students will develop database skills through individual work, team activities, hands-on labs, technical assignments, and a semester-long project.

Attendance and Participation

Regular participation is important because many concepts are developed through hands-on exercises, team problem-solving, demonstrations, and project work.

Daily Deliverables

Short activities such as Warm-Ups, QuickChecks, practice exercises, and reflections provide frequent opportunities to apply concepts and check understanding.

Database Assignments

Individual and team assignments provide more substantial practice with database design, ERDs, normalization, SQL, database implementation, and related technical skills.

Cloud and Platform Labs

Guided labs provide hands-on experience with Google Cloud, BigQuery, Databricks, and other course technologies.

Midterm Hackathon

Students work in teams on an applied database challenge that serves as a midpoint competency checkpoint. The Hackathon emphasizes foundational skills such as requirements interpretation, database design, ERD development, relational thinking, and normalization.

Final Project

Student teams complete a semester-long database development project that progresses through design checkpoints and a final implementation.

Final Project Overview

The Final Project provides an authentic database-development experience in which teams design and develop a database solution for an organizational client or stakeholder.

The project includes three major stages:

Checkpoint 1: Requirements and Conceptual Design

Teams define project scope, identify client requirements and business rules, and develop an initial entity-relationship diagram.

Checkpoint 2: Logical Design and Data Preparation

Teams revise their earlier design, create a relational schema, demonstrate appropriate normalization, and prepare representative data for implementation.

Final Submission and Client Handoff

Teams implement a working database solution, develop useful queries and reports, incorporate appropriate application features, and communicate how the completed system addresses client needs.

The Final Project also includes a data-analysis and visualization component. Students may be asked to demonstrate or explain portions of team work individually as evidence of personal understanding.

Feedback and revision are an intentional part of the project development process.

Detailed project requirements and specifications will be provided in Canvas.

The Final Project is due Saturday the last day of the semester. There is no final exam for this course.

Evaluation of Student Performance

This course uses specifications grading.

Rather than earning a final grade by accumulating points across weighted categories, students demonstrate achievement by meeting clearly defined course requirements.

The final course grade is determined by the highest grade bundle for which all requirements are satisfied.

For example, if a student meets the A-level requirements in every area except Attendance, where the B-level requirement is met, the final course grade is a B.

Canvas may record specifications-based activities as Complete or Incomplete.

• Complete means the work meets the required specifications.
• Incomplete means the work is missing, substantially unfinished, or does not demonstrate the required specifications.

An Incomplete does not automatically indicate that revision is available. When a revision opportunity is offered, the instructor will communicate that through Canvas feedback.

Course Grade Bundles

Requirement	A	B	C	D
Attendance	85%+	75%+	65%+	55%+
Daily Deliverables	90% Complete	80% Complete	70% Complete	60% Complete
Database Assignments	85% Complete	75% Complete	65% Complete	55% Complete
Cloud & Platform Labs	90% Complete	80% Complete	70% Complete	60% Complete
Midterm Hackathon	Meets Specifications	Meets Specifications	Meets Specifications	Meaningful participation and completion
Final Project	Meets A-level project specifications	Meets B-level project specifications	Meets core project specifications	Meets minimum essential specifications

Students who do not meet all requirements of the D bundle will earn an F.

Canvas Grades

Canvas may display percentages, assignment scores, or calculated totals during the semester. These values may be useful for monitoring individual work but do not determine the official course grade.

The official course grade is determined by the specifications bundle system, including requirements such as attendance that may limit the highest grade earned.

Students should use the bundle table, Canvas assignment results, and their attendance record to monitor their standing.

Attendance and Participation

This is an active-learning course, and regular attendance is an important part of the learning process.

Class sessions frequently include database exercises, SQL practice, team problem-solving, design activities, demonstrations, and project work that cannot always be duplicated outside of class.

Attendance will be recorded in Canvas using Roll Call.

Attendance requirements are:

• A Bundle: 85% or higher
• B Bundle: 75% or higher
• C Bundle: 65% or higher
• D Bundle: 55% or higher

The course does not maintain separate categories for excused and unexcused absences. These thresholds provide reasonable flexibility for ordinary illness, appointments, emergencies, and other occasional absences.

If exceptional circumstances significantly affect attendance over an extended period, students should contact the instructor as soon as practical. Additional flexibility may be considered at the instructor's discretion.

Attendance and Daily Deliverables are separate requirements.

Depending on the activity, a Daily Deliverable may be due:

• before class;
• by the end of class; or
• later the same day.

The due date and time shown in Canvas will indicate the expectation for each activity.

TOKEN Policy

This course uses a TOKEN system to provide reasonable flexibility when things do not go as planned.

Each student begins the semester with 5 TOKENS.

One TOKEN may be used for one eligible recovery opportunity, such as:

• submitting an eligible missed or late assignment; or
• completing an additional attempt on eligible work when permitted by the instructor.

TOKEN-eligible work generally includes Daily Deliverables, Database Assignments, and Cloud & Platform Labs, unless an assignment is specifically identified as non-tokenable.

TOKENS may not be used for:

• Attendance;
• the Midterm Hackathon;
• Final Project Checkpoints; or
• the Final Project.

TOKENS do not add points, change percentages, or directly raise a course grade.

Token Distribution Record

Canvas includes an ungraded Token Distribution Record used to track TOKEN allocation and use.

Students should use this record to monitor remaining TOKENS and communicate with the instructor about TOKEN-supported work.

Unused TOKENS have no point value and do not provide extra credit.

All TOKEN-supported work must be completed by Saturday of week 14, unless otherwise approved by the instructor. No work will be accepted after this date!

Late Work and Revisions

Because this course uses specifications grading, late work is not reduced by percentage penalties.

Eligible missed or late work may be completed using a TOKEN. Without a TOKEN, missed or late TOKEN-eligible work will normally remain Incomplete.

Revision Opportunities

Revision opportunities are provided at the instructor's discretion and are intended to support learning when a student has made a timely, substantive, good-faith attempt that does not yet meet one or more specifications.

A submission that is late, substantially incomplete, or demonstrates insufficient effort is not automatically eligible for revision.

When revision is permitted, the instructor will indicate this in Canvas feedback. Revisions must normally be submitted within 5 calendar days of receiving that feedback unless another deadline is specified.

Revision opportunities are not guaranteed for every assignment and should not be viewed as substitutes for careful, timely first submissions.

Major course checkpoints may include structured revision as part of the learning and database-development process.

The Final Project is normally not eligible for revision after its final deadline.

Exceptional circumstances may be considered by the instructor.

Use of Artificial Intelligence in This Course

Generative AI tools such as ChatGPT, Microsoft Copilot, and similar systems are increasingly used in professional data and technology work. Students are encouraged to learn how to use these tools responsibly.

The goal is to use AI to support learning - not to replace the thinking and technical skills this course is designed to develop.

Appropriate Uses of AI

Unless otherwise stated for a particular assignment, students may use AI to:

• explain database concepts in different ways;
• help generate practice questions;
• brainstorm business rules, entities, attributes, or database requirements;
• explain SQL syntax or error messages;
• help debug SQL queries or database code;
• suggest alternative approaches to a database problem;
• review work for clarity or completeness;
• assist with documentation and professional writing; and
• help understand unfamiliar software features.

Students remain responsible for evaluating AI-generated information and determining whether it is correct and appropriate.

Demonstrating Understanding

Submitting AI-generated work that a student cannot explain does not demonstrate competency.

Students should be prepared to explain database designs, SQL queries, relationships, normalization decisions, and other technical work they submit.

The instructor may ask students to explain or demonstrate portions of individual or team work.

AI output may contain incorrect SQL, poor database designs, inappropriate relationships, or other errors. Students are responsible for testing and validating AI-generated material before submitting it.

Documenting AI Use

For substantive assignments and project work, students may be asked to provide a brief AI Use Note identifying:

• the AI tool used;
• what it was used to help with; and
• how the student evaluated, changed, or incorporated the result.

Representative prompts or examples of AI interaction may be requested when useful.

Minor uses such as spelling correction or routine grammar assistance generally do not require documentation unless otherwise stated.

Some activities are intended to provide evidence of individual understanding. AI use may therefore be limited or prohibited for selected Warm-Ups, QuickChecks, competency checks, portions of the Midterm Hackathon, individual project demonstrations, or other activities identified in Canvas.

Students must not submit confidential, private, protected, or sensitive information to public AI systems.

Academic Integrity

Students are expected to complete course work honestly and in accordance with university academic-integrity standards.

Collaboration is encouraged when an assignment is identified as team-based or collaborative. Unless otherwise stated, individual assignments should reflect the student's own work and understanding.

Outside resources, including generative AI, may be used when permitted by course and assignment policies.

Misrepresenting another person's or an AI system's work as evidence of one's own competency may violate course expectations and university academic-integrity policies.

Because this course includes technical and team-based work, students may occasionally be asked to explain, demonstrate, or discuss portions of submitted work to verify understanding.

Classroom Expectations and Communication

This is an active-learning course. Students should come prepared to participate in exercises, team activities, discussions, demonstrations, and project work.

Laptops, phones, and other devices should be used in ways that support learning and the work of the class. Avoid activities that distract you or others.

Professional participation includes:

• contributing appropriately to team activities;
• remaining engaged during demonstrations and discussions;
• treating classmates and their ideas respectfully;
• completing assigned preparation when required; and
• using class time productively for course-related work.

Canvas is the primary source for course announcements, assignments, due dates, instructions, and feedback. Students are responsible for checking Canvas regularly.

Questions about course content and assignments are encouraged.

Questions involving grades, personal circumstances, or other private matters should be directed to the instructor rather than posted publicly.

Students should allow a reasonable amount of time for responses, particularly outside normal working hours and on weekends.

Course Schedule

A detailed Fall 2026 course schedule will be provided separately in Canvas.

The schedule includes course topics, in-class activities, major assignment deadlines, the Midterm Hackathon, Final Project checkpoints, project studio sessions, university breaks, and the Final Project deadline.

The schedule may be adjusted as needed to support student learning. Changes will be communicated through Canvas.

University Policies and Student Support

Required university statements regarding accessibility and accommodations, student support, academic misconduct, Title IX, bias incidents, safety, and other institutional policies should be included here or linked through the university-approved syllabus policy resource.

Students are responsible for reviewing applicable university policies and contacting the appropriate university office or the instructor when assistance is needed.

Late Withdrawal

After the auto-W deadline, withdrawal will be significantly limited, and you will need permission to withdraw and must meet requirements established by O’Neill. See the official IU academic calendar for current dates.

Counseling and Psychological Services

For information about services offered to students by CAPS: http://healthcenter.indiana.edu/counseling/index.shtml

TimelyCare

Indiana students have free, 24/7 access to virtual mental health care services with TimelyCare. Students do not need insurance to access TimelyCare services. https://www.iu.edu/mental-health/find-resources/timely-care.html#0

Religious Accommodation Requests

The “Request for Accommodation for Religious Observances” form must be submitted by the student to the professor no later than two weeks prior to the anticipated absence. A separate form must be submitted for each absence and for each course. Faculty do not have to consider accommodations for the purpose of allowing students to travel away from Bloomington for religious observance. Information: https://vpfaa.indiana.edu/resources/religious-observances-information.html

Accessible Educational Services (AES)

Securing accommodations for a student with disabilities is a responsibility shared by the student, the instructor, and the AES Office. For information about support services or accommodations and required procedures: https://studentlife.indiana.edu/care-advocacy/iub-aes/index.html

Sexual Harassment / Sexual Misconduct Resources

As your instructor, one of my responsibilities is to help create a safe learning environment on our campus. Title IX and IU Sexual Misconduct policy prohibit sexual misconduct. If you have experienced sexual misconduct, or know someone who has, the University can help. Confidential resources include the Sexual Assault Crisis Service (812-855-8900), Counseling and Psychological Services (812-855-5711), Confidential Victim Advocates (812-856-2469), and IU Health Center (812-855-4011). More information: http://stopsexualviolence.iu.edu/help/index.html. Federal regulations and University policy may require the instructor to convey information about potential sexual misconduct to appropriate Title IX officials, who will work to provide support while protecting privacy to the extent possible.

IU Culture Centers

Information about IU culture centers and communities is available at: https://studentlife.indiana.edu/communities/culture-centers/index.html