<aside> 👯‍♀️

• Instructor: Christopher Seaman - Christopher.Seaman@
• EA's:
  • Samantha Chan - Samantha.Chan2@
  • Eric Yang - Eric.Yang2@
• Dates: January 7th - March 18th, 2026 (11 class meetings)
• Lecture: Wednesday, 1:00 PM - 3:00 PM, Mission Hall 1407
• Lab: Wednesday, 3:00 PM - 4:30 PM, Mission Hall 1407
• GitHub: https://github.com/christopherseaman/datasci_223
• CLE: https://courses.ucsf.edu/course/view.php?id=12675 </aside>

Grading

This is graduate school. The course will ask effort of you but that doesn’t mean the grading will be harsh. Do the work and you’ll get a good grade.

Assignments (60%)

• Pass/Fail submitted individually
• Autograded but human-reviewed on failure
• “Due” the following week
• Accepted late without penalty (email if submitting late)
• No assignments for week 9-11 (focus on project)

Final Project (40%)

• Open-ended in format
• May work individually or in groups of any size
• Should probably use methods from this course

Course Topics

Winter 2026 - 11 Lectures

Foundational (1-4) → ML/AI (5-8) → Student Choice (9-11)

1. Setup + Debugging - Notebook hygiene, defensive programming, VS Code debugger
2. Larger-than-Memory Data - Polars lazy evaluation, out-of-core processing, parquet
3. SQL for Data Analysis - SELECT, JOIN, GROUP BY, window functions, pandas integration
4. NLP Foundations - Text preprocessing, embeddings, sentiment, clinical text applications