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The official list of prerequisites is:
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Data 8.
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CS 61A or CS88 or Engineering 7. We strongly recommend either CS 61A or CS 88.
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EE 16A or Math 54 or Stat 89A. This may be satisfied concurrently with Data 100, but we strongly recommend that you finish a linear algebra course before taking Data 100.
The official prerequisites are being strictly enforced by CDSS. Decisions on any requests for exceptions to this policy are being made by the CDSS advisors. To request an exception see the Data Science Program's 2023 Spring Courses Enrollment page. Do not email instructors about this as we are not making these decisions at all.
I am a graduate student but the system won't let me enroll in Data 100 due to missing prerequsites, what can I do?
Graduate students should use the C200 class code, not C100: C200 has a proper graduate code so you don't have undergraduate prerequisite issues. There will be some differences in grading and homework, but the main content is the same.
I can't enroll yet for reason X (waitlist, concurrent enrollment, etc) but expect/hope to, what should I do?
We encourage you to not fall behind: you should complete all work when it is assigned. We generally will not provide additional accommodations to students who enroll late; please see below for more details.
To be added to the relevant resources (Ed, bCourses, etc.), please email {{page.course.email}} (Subject line: Interim access to class resources) with your name, email and student ID.
To get a section (discussion and/or lab) assignment, please contact {{page.course.logistics_ta}}.
Historically we've been able to accomodate students who wish to enroll, so we do not expect any issues allowing waitlisted students once the usual early-semester shuffle takes place. We will update this page if conditions change.
We recommend that you sign up for the class EdStem link TBD and follow the class assuming you'll have a slot soon. See above for how to get added to any other required resources.
I have a Concurrent Enrollment Request that hasn't been approved yet, what should I do? What are my chances of getting into this class?
By campus policy, we must first process the waitlist in its entirety before we can admit you. We will do all we can to ensure all waitlisted students are promptly admitted, so that you can also enroll in the course, though we can not provide 100% guarantee that will be the case. But we strongly suggest that you start working on the course, following lectures and turning in assignments, from the start. This will prevent you from falling behind and will give you the best chance of success in the class.
Note that once all CE requests are processed (in the first few weeks of classes), we will remove all temporarily added students who do not end up with a permanent enrollment in the course.
See above for how to get added to any other required resources.
Most class materials are available online to all campus students, and you can access the lecture videos, slides, etc. without limit, as well as use the online Data 100 DataHub (our JupyterHub instance for this class).
Note however that the class EdStem, our only other class resource, is reserved for enrolled students and faculty auditors, as homework details and solutions are discussed in that environment.
I have another enrollment issue - can the instructors assist me, make an exception for my case or provide me with an "enrollment code"?
No. The instructors do not handle any enrollment issues. If you need assistance with enrollment or think your circumstances warrant exceptional consideration, you must discuss those with a Data Science enrollment advisor at {{page.course.enrollment_email}}. They are the only ones authorized to handle such situations and enroll you.
We will offer:
- Live hybrid lectures, where remote students can participate.
- Recordings of all lectures, available for online viewing shortly (by EOD) after the lecture is delivered.
- Discussions will be mostly in-person, but we will offer a small number of online discussion sections.
- There will be no synchronous lab sessions: labs will be due on Tuesdays and a video walk-through of the lab content will be released after the lab is due for you to watch on your own. There will be online help available for labs through office hours and dedicated Ed channels.
The basic policy is the following:
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TIME CONFLICTS: This class WILL allow for time conflicts with lecture, but students must attend their synchronous discussion or lab (will be assigned after instruction begins).
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EXAM CONFLICTS: This class WILL NOT accommodate final exam conflicts. Midterm conflicts will be arranged via a midterm accommodation form sent out several weeks before the exam date.
Exams will be in-person (not online) and subject to standard proctoring procedures, with appropriate accommodations for DSP students.
The exact midterm exam dates will be announced on the first date of class. Exams will likely be in the evening, i.e. not during lecture time.
As per the Campus Registrar, the final exam will be held {{page.course.final_exam_slot}}. The exam will be held in person, not online.
The course only allows for time conflicts for specific courses that have been pre-approved for conflicts, after planning with course instructors of those courses and the Division. This will take through the beginning of January while course policies are decided across all courses with final conflicts. No accommodations will be made for travel.
Update (January 15, 2023): Data 100 will NOT offer an alternate final as the course exam schedule does not conflict with any CS/DATA courses.
Discussion sections and sign-ups will be released within the first two days of class. See our course policies for more information about labs.
Where can I find links to the class schedule, optional textbook, and other additional relevant resources?
The course website for Spring 2023 will be released soon.
Here. If that link doesn't work for you, please email {{page.course.email}} to be added. You will only be added if you are an enrolled student, or pending enrollment in the first few days. Note that we will purge the Ed roster once early-semester enrollment settles down.
If your question isn't answered in this FAQ, please try EdStem next before emailing the instructors.
We will only be using bCourses for synchronizing access to Gradescope. As long as you are enrolled in the class right now, you don't need anything. All class materials are available online through the website; communications happen on EdStem; and computational work is done on the DataHub.
The answer to the first question (Can I catch up?) is impossible to give in general, as it depends both on how late you add the course, and what your own background is. But as a general rule, the course moves quite quickly and covers new ground from the start. We estimate most students will probably struggle to catch up effectively if they join any later than the first few days of the semester, unless they have a particularly strong background already in the initial topics (probability, SQL, Python and Pandas).
As for the second part of this question (Can I be excused for all late work?), we will not be accommodating extensions beyond our extension policy below (see next question for details).
If you are currently waitlisted/on the fence for adding this course, please see the Enrollment section above.
We will be updating our individual extension policy soon.
If there's a problem that affects the whole class (e.g. a hub outage), we'll make announcements on Ed and will change the deadline for the whole class.
We only allow posts made on EdStem and you must make it yourself; we do not make posts on your behalf. These are our guidelines for EdStem posts from student groups - they must be text-only posts (no videos) where the announcement:
- Has to do with teaching or tutoring in a non-commercial setting, or
- is directly related to the material in the course, or
- is seeking to recruit students to help with the public good in a non-financial way (no solicitations for donations!), and in a context explicitly connected to their work in the course.
All applications for Academic Student Employee positions are managed centrally; you can find all the details here. Please do not email the instructors individually with your resume/etc, as they are not in a position to hire you.
Please e-mail the course staff at {{page.course.email}} and one of the instructors will get back to you. Note that to ensure more timely responses, this address is monitored by the team of the two lead instructors (Narges Norouzi and Lisa Yan) and the Head TAs ({{page.course.head_ta}}), as well as several lead GSIs to ensure more timely responses. You can contact Narges and Lisa directly for matters that require strict privacy and their direct attention.
We’re excited to have you in the class this Spring!
Lisa, Narges, and the rest of your Spring 2023 instructional team.