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If you are a Concurrent Enrollment student, you can find more information [here](ADD CORRECT LINK). -->
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# Frequently Asked Questions for Summer 2025 Data 100
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# Frequently Asked Questions for Fall 2025 CS 189/289A
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{: .no_toc }
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## Table of Contents
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---
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## I need an enrollment code/enrollment is too limited/I'm on the waitlist/etc.
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The instructors are not able to provide permission codes or any new information about enrollment in Data 100 at this time. Please refer to the [Data Science Summer 2025 Courses page]({{page.course.classes_page}}){:target="_blank"} and to the [Data 001 Ed]({{page.course.edstem}}){:target="_blank"}, which includes all information currently available about enrolling in Summer 2025 classes. If you are not able to find an answer to your question there, please email [ds-advising@berkeley.edu](mailto:ds-advising@berkeley.edu).
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The instructors are not able to provide permission codes or any new information about enrollment in CS 189/289A at this time.
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<!-- TODO -->
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---
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## What are the official prerequisites for this course?
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The official prerequisites are:
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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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- Math 54, EECS 16A, Math 56, Math 110, 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.
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-**Multivariable Calculus**: MATH 53
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-**Linear Algebra**: MATH 54 or equivalent
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-**Probability and Discrete Mathematics**: COMPSCI 70 or equivalent
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As with other enrollment matters, the instructors have no say on this matter, if you have _any_ questions regarding prerequisites, please email [ds-advising@berkeley.edu](mailto:ds-advising@berkeley.edu).
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As with other enrollment matters, the instructors have no say on this matter, if you have _any_ questions regarding prerequisites, please email [cs-advising@berkeley.edu](mailto:cs-advising@berkeley.edu) (CS students) or [eecs-advising@berkeley.edu](mailto:eecs-advising@berkeley.edu) (EECS students)
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## Will the official prerequisites be enforced?
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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 Summer 2025 Courses page]({{page.course.classes_page}}). _Do NOT email instructors about this_; we do not make these decisions.
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The official prerequisites _are being strictly enforced by the department_. Decisions on any requests for exceptions to this policy are being made by the EECS advisors. _Do NOT email instructors about this_; we do not make these decisions.
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<!-- ---
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## I am a graduate student, but the system won't let me enroll in Data 100 due to missing prerequisites; what can I do?
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## I am waitlisted. What should I do? What are my chances of getting into this class?
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<!-- As of November 5, 2024, we are not considering expanding the course. -->
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We will update this page if we plan to expand the course. In case we expand the course, it will happen before the first lecture.
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We will decide during the first couple weeks of courses about whether to expand the course. Please do not reach out to the instructors about getting off of the waitlist before then. If you are on the waitlist, you are expected to keep up with course material.
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<!-- TODO -->
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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 permanent enrollment in the course.
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See above for how to get added to any other required resources.
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---
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## I am a campus student who wants to audit the course. What can I do?
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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)](https://data100.datahub.berkeley.edu){:target="_blank"}.
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Most class materials are available online to all campus students, and you can access the lecture videos, slides, etc., without limit.
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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. Furthermore, we do not add auditors to bCourses, Pensieve, etc., as our limited instructional resources are reserved for officially enrolled students.
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We will offer:
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- Recordings of all lectures are available for online viewing shortly after the lecture is delivered. Note these will _not_ be live hybrid lectures where remote students could participate, only static recordings, available post-lecture.
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- Discussions will be primarily in-person, but we will offer a small number of online discussion sections.
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- There will be no direct lab sessions: Labs will be due on Mondays and Thursday, and a video walk-through of the lab content will be released before 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.
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- Discussions will be in-person, but we will make discussion worksheets, solutions, and some other resources available to all students.
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## What is the proctoring/accommodation policy for exams?
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<!-- **Midterm** on **Wednesday March 12** from 8pm-10pm -->
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We plan to administer the midterm in one of the computer-based testing facilities on campus on 7/17 and 7/18. Enrolled students will be able to sign up for a two-hour time slot to take the midterm during one of the two available days. More information will be provided shortly after the semester begins, including instructions for students with DSP accommodations.
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More information will be provided shortly after the semester begins, including instructions for students with DSP accommodations.
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<!-- TODO -->
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---
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## What is the final exam schedule for this class?
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<!-- **Midterm** on **Wednesday March 12** from 8pm-10pm -->
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The final exam will be administered EITHER on-paper during regular lecture time (11:30am - 1pm) on 8/13 AND 8/14 (i.e., a two-part exam), OR in one of the computer-based testing facilities (CBTF) on campus with the exam completed in ONE sitting on 8/13, 8/14, or 8/15.
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More information will be provided shortly after the semester begins, including instructions for students with DSP accommodations.
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You will not have a choice of final exam modality (i.e., on-paper or computerized). The instructors will choose a single exam modality for the entire class. We will announce our decision shortly after the midterm.
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* FINAL EXAM CONFLICTS: This class will **NOT** accommodate final exam conflicts.
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## I have a conflict for the final exam, what can I do?
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**The course does not allow for time conflicts.** Additionally, no accommodations will be made for travel.
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## When will the full schedule of labs and discussions be announced?
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## When will the full schedule of discussions be announced?
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Discussion sections and sign-ups will be released within the first two days of class. Discussions will be on Mondays from 1-5pm and Wednesdays from 1-5pm. There will be no direct lab sessions. See our course policies for more information about labs.
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Discussion sections and sign-ups will be released within the first week of class.
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## Where can I find links to the class schedule, optional textbook, and other relevant resources?
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<!-- The Summer 2025 course website is not yet live. The Spring 2025 website can be found [here](https://ds100.org/sp25/). -->
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The course website for Summer 2025 is linked [here]({{page.course.website}}).
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The course website for Fall 2025 is linked [here]({{page.course.website}}).
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Check out the [course homepage]({{page.course.website}}) and the [resources page]({{page.course.website}}/resources). Don’t hesitate to make suggestions by filing an issue on the [class repository]({{page.course.repo}}) (or even better, propose a new pull request with your additions!).
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Check out the [course homepage]({{page.course.website}}) and the [resources page]({{page.course.website}}/resources).
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## Where is the Class EdStem?
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<!--The EdStem is not yet live. -->
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The EdStem will be made live in the first week of class.
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[Here]({{page.course.edstem}}).
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If that link doesn't work for you, please email [{{page.course.email}}](mailto:{{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.
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If your question isn't answered in this FAQ, please try EdStem next before emailing the instructors.
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## I can't find any information on bCourses. Why?
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The answer to the first question 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 (Python and Pandas).
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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).
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_If you are currently waitlisted/on the fence for adding this course,_ please see the [Enrollment](#enrollment) section above.
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<!-- [//]: #(As for the second part of this question - we will make special considerations for late work based on the date you joined the) course, and were added to relevant course platforms. Please email Data100Departmental [{{page.course.email}}](mailto:{{page.course.email}}) if this applies to you, and we'll communicate separately.
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[//]: #(To get a section (discussion and lab) assignment, please contact {{page.course.logistics_ta}}.) -->
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## I didn't turn in a Lab/HW/etc in time because I had X (illness, etc.). Can I have an extension?
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## I didn't turn in a HW in time because I had X (illness, etc.). Can I have an extension?
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We have a generous slip days policy for assignments, plus two lab drops. This is meant to allow most students to manage delays that can occur due to illness, accidents, personal problems, etc. You _must use your slip days first for any and all circumstances that cause you to be late with an assignment_. You should not think of slip days as "free extra days to be late any time."
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We have a generous slip days policy for assignments. This is meant to allow most students to manage delays that can occur due to illness, accidents, personal problems, etc. You _must use your slip days first for any and all circumstances that cause you to be late with an assignment_. You should not think of slip days as "free extra days to be late any time."
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We will only provide an extension in case of a _major and documented_ set of exceptional circumstances (death in the family, illness documented with medical paperwork, or similar).
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In such case, please fill out the Additional Accommodations Form on the course website.
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2. is directly related to the material in the course, or
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3. 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.
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<!-----
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## I would like to apply for a (u)GSI position for this course. What should I do?
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All applications for Academic Student Employee positions are managed centrally; you can find all the details [here](https://cdss.berkeley.edu/data-science/student-opportunities/joining-data-course-staff). Please _do not_ email the instructors individually with your resume/etc, as they are not in a position to hire you.
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All applications for Academic Student Employee positions are managed centrally; you can find all the details [here](https://cdss.berkeley.edu/data-science/student-opportunities/joining-data-course-staff). Please _do not_ email the instructors individually with your resume/etc, as they are not in a position to hire you.-->
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## Who do I contact if I have further questions? How do I email the instruction team?
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Please e-mail [{{page.course.email}}](mailto:{{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 (Josh Grossman and Michael Xiao), the Head TAs, and multiple lead GSIs. You can contact Josh and Michael directly for matters that require strict privacy and their direct attention.
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<!-- Please e-mail [{{page.course.email}}](mailto:{{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 (Josh Grossman and Michael Xiao), the Head TAs, and multiple lead GSIs. You can contact Josh and Michael directly for matters that require strict privacy and their direct attention. -->
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We’re excited to have you in the class!
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Josh, Michael, and the rest of your Summer 2025 instructional team.
This class covers theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; deep learning models including CNNs, Transformers, graph neural networks for vision and language tasks; and Markovian models for reinforcement learning and robotics.
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<!-- TODO: Add previous course offerings. (We can't do this the way Data100 has been. I know how to link all the Spring versions, but I'd have to hunt down Fall versions.) -->
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