Welcome to DSC 291 in Winter 2023! This page should answer most of the questions you might have about how the course is run.
Lecture 15 uploaded
Lectures 8-14 uploaded
HW2 uploaded in Canvas. Due 02/08/2023
Lectures 6-7 uploaded
HW1 is uploaded in Canvas. Due 1/25/2023
Lectures 1-5 uploaded
This semester DSC 291 AFDS is taught by Prof. Arya Mazumdar
email: arya@ucsd.edu
This term there are three TAs for DSC 291 AFDS
Rajeshwari Sah <rasah@ucsd.edu>
Sai Srikar Vaidyula: svaidyul@ucsd.edu
Ramtin Hosseini <rhossein@eng.ucsd.edu>
Class Location: PETER 104
Time: TuTh 9:30 - 10:50 AM
The lecture slides and/or any notes will also be posted on the lectures page below.
To get started in DSC 291 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.
Mon - 11:00am - 12:00pm
Tue - 12:00pm - 1:00pm
Thu - Prof. Mazumdar -11:00am -12:00pm SDSC E206
The Zoom link for the Office Hours is https://ucsd.zoom.us/j/96472610535. The Monday and Tuesday OHs will be taken by one of the TAs. The schedule can be found here.
Homeworks will be distributed bi-weekly on Wednesdays, and will be due the next Wednesday, 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 18 due Jan 25
HW2 out Feb 1 due Feb 8
HW3 out Feb 22 due Mar 1
HW4 out Mar 8 due Mar 15
There will be two exams. There will be no scheduled lecture, if any, on exam days.
Exam 1 covered lectures 1-10: Feb 16 in class.
Exam 2 will cover materials of lectures 11-19. During finals week.
The final grading will be done based on the following breakdown of contribution.
Homework: 50%
Exam 1: 25%
Exam 2: 25%