Course DescriptionAbout this course
Databases are at the heart of modern application development, and their use extends to many domains where large amounts of data must be stored for efficient update, retrieval, and analysis. This course provides a comprehensive introduction to data management, covering the relational data model, SQL, formal query languages, query evaluation, data storage and indexes, NoSQL databases, conceptual design, integrity constraints, design theory, normal forms, and data quality.
Through pre-recorded video lectures, demos, and hands-on exercises, students gain a strong foundation in database-management concepts and practices, with a focus on SQL and the relational model. The course begins with the relational data model and SQL basics, moves through advanced SQL, formal query languages, and query evaluation, then covers data storage and indexing, NoSQL, conceptual design, integrity constraints, design theory, and normal forms, closing with data quality. Upon completion, students will be equipped to design, query, and maintain a relational database.
CalendarSchedule & Materials
All lecture videos for the term are released and available below — the exact slides and recordings from the fully-remote summer offering. Subject to change.
| Week | Topic | Slides & Recordings | Assignments | Discussion / Notes |
|---|---|---|---|---|
| Week 1 Jun 29 – Jul 3 |
Lecture 1 — Course Intro, Data Models & SQL Basics Reading: GMUW §2.1, 2.2, 2.3 |
— | — | |
| Week 1 Jun 29 – Jul 3 |
Lecture 2 — Joins & Aggregates in SQL Reading: GMUW §6.1, 6.2 |
DatasetData
|
— | SQL practice Recording |
| Week 2 Jul 6 – Jul 10 |
Lecture 3 — Advanced SQL: Grouping, Query Evaluation & Nested Queries | Due WQ1 — Intro, Data Models & Simple SQL Due HW1 — SQLite |
— | |
| Week 3 Jul 13 – Jul 17 |
Lecture 4 — Formal Query Languages: Relational Algebra | Due HW2 — Basic SQL Due WQ2 — SQL Aggregates |
Relational Algebra SourceRecording | |
| Week 4 Jul 20 – Jul 24 |
Query Evaluation & Basics of Indexes | — | — | |
| Week 5 Jul 27 – Jul 31 |
Lecture 7 — Cost Estimation & NoSQL Databases | — | Indexes & Cost Estimation Source | |
| Week 5 Jul 27 – Jul 31 |
Lecture 8 — Conceptual Design: ER, Constraints & Design Theory Reading: GMUW §4.1, 4.6 |
— | Homework Solutions | |
| Week 5 Jul 27 – Jul 31 |
Lecture 9 — Normal Forms: Boyce–Codd Normal Form (BCNF) | Due HW3 — Advanced SQL Due WQ4 — RA & Conceptual Design Due HW4 — Conceptual Design |
Conceptual Design practice Recording | |
| Final Jul 31, 2026 |
Final Exam — Comprehensive Remote. Due Jul 31, 2026 (on Canvas). |
— | Covers all course material | — |
Some slides are adapted from the University of Washington database group.
Extra MaterialDemo Notebooks — from the Spring 2026 offering
Supplementary interactive Colab notebooks from the most recent (Spring 2026) offering, provided as optional extra hands-on practice alongside the summer lectures.
Instructional TeamWho's teaching
Babak Salimi
Instructor · bsalimi.github.io
Office hours: Wednesdays, 4:00–5:00 p.m. PT (Zoom — link on Canvas)
Haoquan Guan
Teaching Assistant
Office hours: Thursdays, 3:00–4:00 p.m. PT (Zoom — link on Canvas)
AssessmentGrading
The grading scheme is a hybrid of absolute and relative grading. Absolute cutoffs are based on your total score; relative bins are based on your position in the class distribution. The better of the two grades is your final grade.
Weighting
- 50% Homework — written/programming assignments (45%) + web quizzes (5%)
- 50% Final Exam — comprehensive, remote
Homework may be done in teams of up to 2; each member must contribute equally and understand the submitted work.
Grade Cutoffs
| Grade | Absolute (≥) | Relative bin |
|---|---|---|
| A+ | 97 | Highest 5% |
| A | 94 | Next 10% (5–15) |
| A− | 91 | Next 15% (15–30) |
| B+ | 85 | Next 15% (30–45) |
| B | 80 | Next 15% (45–60) |
| B− | 70 | Next 15% (60–75) |
| C+ | 65 | Next 5% (75–80) |
| C | 60 | Next 5% (80–85) |
| C− | 55 | Next 5% (85–90) |
| D | 50 | Next 5% (90–95) |
| F | < 50 | Lowest 5% |
What you'll doCourse Workload
Subject to change. Weekly assignments combine written problem-solving and programming with short web quizzes that reinforce lecture material.
Homework
- HW 1 — SQLite
- HW 2 — Basic SQL
- HW 3 — Advanced SQL
- HW 4 — Conceptual Design
Web Quizzes
- WQ 1 — Intro, Data Models & Simple SQL
- WQ 2 — SQL Aggregates
- WQ 4 — Relational Algebra & Conceptual Design
Due dates appear on the Canvas calendar.
ExpectationsPolicies
Academic Integrity
Students are expected to adhere to UC San Diego's academic integrity policies. You may discuss problems with classmates, but all submitted work must be your own. Violations will be reported to the Academic Integrity Office.
AI / LLM Policy
Use of AI tools (ChatGPT, Copilot, etc.) is allowed for learning and studying, but not for directly generating homework solutions. You must understand and be able to explain everything you submit.
ReferenceResources
Textbook
Database Systems: The Complete Book — Garcia-Molina, Ullman & Widom, 2nd ed., Prentice Hall, 2008. (Optional — slides and recordings are sufficient.)
Canvas
canvas.ucsd.edu
Grades, submissions & announcements.
Piazza
piazza.com/ucsd/summer2026/dsc100
Q&A forum (access code on Canvas).
Related groups: UCSD Database Group · SIGMOD · VLDB · PODS. For more on data science at UC San Diego, visit the Halıcıoğlu Data Science Institute.