Welcome to DSC 206 in Winter 2025! This page should answer most of the questions you might have about how the course is run.
This semester DSC 206 AFDS is taught by Prof. Arya Mazumdar
email: arya@ucsd.edu
This term the TA for DSC 206 AFDS is
Xiaxin Li <xil095@ucsd.edu>
Class Location: CENTR 216
Time: TuTh 11:00 AM - 12:20 PM
The lecture slides and/or any notes will also be posted on the lectures page below.
To get started in DSC 206 AFDS, 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. You should have received an email invitation for Gradescope, but if not please let us know as soon as possible (preferably via Campuswire).
Some aspects of the course, like office hours and the remote discussion, will be held using Zoom. You should already have an account through UCSD; see the Zoom guide for more help. Note that you will not be expected to have a webcam!
We'll use Canvas for the course gradebook and some announcements.
No materials are required for this course, as well as our own course notes. That said, here are some books that you might find useful.
Mainly the following book will be followed.
Avrim Blum, John Hopcroft, and Ravindran Kannan: Foundations of Data Science. Link.
We will also refer to the following book.
Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets. Link.
Both the books are available freely, follow the links.
The Instructors and TA/Graders will be available to answer your questions every week. All times below are PDT.
TA's office hours: Tuesdays 2-3pm, or by appointment.
Location: https://ucsd.zoom.us/j/5465321979
Homeworks will be distributed bi-weekly on Thursdays, and will be due the next Thursday, by midnight. The expected number of homeworks will be four.
Lowest Score Dropped: Your homework with the lowest score will be dropped when calculating the final grade. For this reason, late homeworks will not be accepted under any circumstances.
HW1 out Jan 16 due Jan 23
HW2 out Jan 30 due Feb 6
HW3 out Feb 20 due Feb 27
HW4 out Mar 4 due Mar 11
There will be two exams. There will be no scheduled lecture, if any, on exam days.
Exam 1 covered lectures 1-10: Feb 13 in class.
Exam 2 will cover materials of lectures 11-19: March 13 in class.
The final grading will be done based on the following breakdown of contribution.
Homework: 50%
Exam 1: 25%
Exam 2: 25%