DSC 206
Algorithms for Data Science
Tue / Thu: 9:30am -- 10:50am, PODEM 1A19
Tue / Thu: 9:30am -- 10:50am, PODEM 1A19
Welcome to DSC 206 in Spring 2026!
Prof. Yusu Wang (Office: HDSI 446)
email: yusuwang@ucsd.edu
Samantha Chen (email: sac003@ucsd.edu )
The lecture slides will also be posted in Canvas.
Topic 1: Dimensionality Reduction
To get started in DSC 206, you'll need to set up accounts on a couple of websites.
We'll be using Campuswire as our course message board. Campuswire is like Piazza, but unlike Piazza, Campuswire does not sell student data to third parties. You should have received an invitation via email, but if not you should get in touch with a course staff member as soon as possible, as we'll be making all course announcements via Campuswire.
If you have a question about anything to do with the course — if you're stuck on a homework problem, want clarification on the logistics, or just have a general question about data science — you can make a post on Campuswire. We only ask that if your question includes some or all of an answer, please make your post private so that others cannot see it. You can also post anonymously if you would prefer.
Course staff will regularly check Campuswire and try to answer any questions that you have. You're also encouraged to answer a question asked by another student if you feel that you know the answer.
We'll be using Gradescope for homework submission and grading. Most of the assignments will be a mixture of math and coding, and the coding parts are usually autograded via Gradescope., You should have received an email invitation for Gradescope, but if not please let us know as soon as possible (preferably via Campuswire).
Lecture notes and homework will be available in course Canvas.
No materials are required for this course; we'll use lecture notes as the main resource. That said, here are some books that you might find useful.
Avrim Blum, John Hopcroft, and Ravindran Kannan: Foundations of Data Science
Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets
You can download the syllabus here.
Prof. Wang's Office Hour is Tuesdays 11:00am -- 11:50am in HDSI 446.
Samantha Chen's Office Hour is Thursdays 1:00am -- 3:00pm in HDSI 432
There will be two exams.
Midterm: May 7 (Thu), 2026, in class (no regular class on that day)
Final: June 9th, 2024, 8am -- 10am, (University Registra Scheduled final exam time).
You are allowed to use LLMs (e.g., chatGPT) to help with learning course materials. You are not allowed to directly input homework problems to LLMs. You are allowed to discuss with your classmates. However, it is important that you have to write your solutions independently. So if you discuss with your classmates or use LLMs to help learn concepts, you need to then write solutions separately and independently.
Slip Days: Each student has 2 slip days for the entire quarter. Each slip day allows you to submit your homework 24 Hours later than the deadline. There are no other exceptions than the Slip Days.
The final grading will be done based on the following breakdown of contribution.
Homework: 32%
Midterm: 30%
Final Exam: 38%