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Syllabus 📖

Table of contents

  1. Preface
  2. About 🧐
  3. Communication 💬
  4. Technology 🖥
  5. Course Structure 🍎
    1. Lecture
    2. Discussion
    3. Homeworks
    4. Weekly Surveys
    5. Office Hours
    6. Weekly Schedule
  6. Exams 🧪
    1. Redemption Policy
  7. Policies ✏️
    1. Grading
    2. Late Policy, Slip Days, and Drops
    3. Regrade Requests
    4. Incomplete Grades
    5. Academic Integrity
    6. A note on letter grades
  8. Support 🫂
    1. Accommodations
    2. Diversity and Inclusion
  9. Acknowledgements 🙏


As of 9/17, the contents of this syllabus are still tentative, and subject to change.

This is a long document! But we expect you to read it in its entirety at the start of the quarter, as it covers important details that we won’t necessarily discuss in class.

One thing we’ll make clear here is that while lectures and some office hours will be held in-person, it is possible to take this class without needing to physically come to campus. See below for details.

About 🧐

  • How do we know if an avocado is going to be ripe before we eat it? 
  • How do we predict the salary of a future data scientist?
  • How do we teach a computer to read handwriting?

The world is increasingly recognizing the value of data in solving complex and open-ended problems such as these. Instead of explicitly telling the computer exactly how to differentiate between the letters of the alphabet, we instead give the computer many examples of each letter and let it learn the differences automatically. Similarly, by identifying patterns in data, we can learn which factors combine to make an avocado ready-to-eat or a person likely to be a successful data scientist. The explosive growth of data science is largely due to the fact that this approach of learning from data often works remarkably well.

But how do we learn from data? This is the central question of DSC 40A. We will see that virtually every rigorous learning method involves two steps: 1) turning the abstract problem of learning into a concrete math problem; and 2) solving that math problem. This quarter, we will see how to apply this fundamental approach in a variety of contexts. After this class, you will understand the basic theoretical principles underlying almost every machine learning and data science method — from simple linear regression to deep neural networks. You’ll also be better prepared to tackle the math you’ll see in your upper-division courses, like vector calculus, linear algebra, and probability.

Communication 💬

This quarter, we’ll be using Campuswire as our course message board. Campuswire is like Piazza, but unlike Piazza, You will be added to Campuswire automatically; email Suraj ASAP if you’re not able to access it, as we’ll be making all course announcements through it.

If you have a question about anything to do with the course — if you’re stuck on a assignment 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 will not be using Canvas much, if at all, this quarter; this website, Campuswire, and Gradescope (see Technology) will serve as replacements.

Technology 🖥

We will be using several websites this quarter. Here’s what they’re all used for:

  • Course Website: where all content will be posted.
  • Campuswire: discussion forum where all announcements will be sent, and where all student-staff and student-student communication will occur. You should be automatically added to Campuswire; let us know if that’s not the case.
  • Gradescope: where all assignments are submitted and all grades live. You should be automatically added to Gradescope; let us know if that’s not the case.
  • Zoom: the virtual conferencing platform we will use for some office hours. 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!

If you will not have reliable access to a computer this quarter, please reach out to us ASAP, as the university may be able to accommodate you.

Course Structure 🍎


Lectures will be held in-person on Tuesdays and Thursdays from 11AM-12:30PM in Center Hall 109. Attendance will not be required, and recordings will be posted online afterwards. All lecture resources (slides, code if necessary, and readings) will be posted on the course website; these will be your primary resource in this class, as there is no formal textbook.

Lectures will include “Quick Check” questions that we will ask you to answer live; these are not required but are strongly encouraged to check your understanding.


EDIT: As of 10/13, discussion will be held IN PERSON on Wednesdays from 6-7PM in Center Hall 113.

Discussion sections will be used to facilitate small group work with peers. We will provide a worksheet of problems, which should help reinforce concepts from the recent lectures and prepare you to do that week’s homework assignment. In the discussion section on Wednesday evening, you will complete the worksheet in groups of two to four students. Show up and the TA will pair you with other students, though if you want to organize your own group, you may do so. Self-organized groups can also meet at a time that they arrange independently, but you are encouraged to come to the discussion section and do your work there if possible because TA help is available.

Worksheets are due to Gradescope by Thursday at 11:59pm. Only one member of each group should submit the worksheet, and they should indicate the names of all group members on Gradescope. Worksheets won’t be graded on correctness, but rather on good-faith effort. Even if you don’t solve any of the problems, you should include some explanation of what you thought about and discussed, so that you can get credit for spending time on the assignment. In order to receive credit, you must work in a group of two to four students (no more, no less) for at least 50 minutes. You may not do the groupwork alone.


This class will have weekly homework assignments, which will usually be due to Gradescope on Mondays at 11:59pm. We will aim to release homework assignments the Tuesday of the previous week.

Homeworks should be written up and turned in by each student individually. You may talk to other students in the class about the problems and discuss solution strategies, but you should not share any written communication (other than publicly on Campuswire). You can tell someone how to do a homework problem, but you cannot show them how to do it. One way to tell if you are respecting this boundary is to ask yourself whether your collaboration could take place over the phone. Additionally, the content of your verbal communication should involve the problem-solving strategy and approach, and you should not directly compare answers with classmates.

Talking through homework problems with other students can be very valuable for many reasons:

  • You will connect with other students in the class, which is especially important in an online format where much of the learning is done in isolation.
  • You will learn about someone else’s thought process and learn new ways of solving problems that you may not have thought of.
  • You will get practice explaining your ideas, which is a useful life skill, and important for job interviews. You will get practice thinking critically about whether someone’s proposed solution actually works, and you will learn how to poke holes in shaky arguments.

As a result of this collaboration policy, students may have similar approaches to problems, but they should not have similarly presented solutions, such as word choice.

We encourage you to post homework-related questions on Campuswire, though your questions (and answers) should be about approaches, not answers. We also encourage you to come to instructor and staff office hours for help on homework questions.

For each problem you submit, you should cite your sources by including a list of names of other students with whom you discussed the problem. Instructors do not need to be cited.

Weekly Surveys

Since this is a large class with some remote components, we want to give you the opportunity to ask questions, reflect on the content of the lectures, and give feedback. As such, we will have feedback and reflection surveys for you to fill out roughly each week. These forms will be useful for your own learning and consolidation of knowledge, as well as for the instructional staff to get feedback. These will coincide with homework assignments, e.g. Survey 2 and Homework 2 will come out and be due at the same time. These will be hosted on Google Forms, and will be posted on the course homepage. They will generally not be anonymous, though there will be a few points in the quarter for you to provide us with anonymous feedback about the course.

Please note that while we do read every weekly survey, we use these forms to get a picture of the class as a whole, but we do not respond to individual comments and concerns brought up here. If you’d like to initiate a conversation about something, please contact us through Campuswire, as described in the Communication section above.

These are a part of your grade; see the Grading section below.

Office Hours

To get help on assignments and concepts, course staff will be hosting several office hours per week. Some of these will be held remotely and some will be held in person. See the Calendar tab of the course website for the most up-to-date schedule and instructions.

Weekly Schedule

To summarize, here’s what a typical week will look like in the course (note that this is subject to change; the most up-to-date deadlines will be on the course homepage):

Homework and Survey due  Groupwork due 

Exams 🧪

There will be two exams this quarter:

  • Midterm: Thursday, October 21, 11AM-12:30PM
  • Final: Wednesday, December 8, 11:30AM-2:30PM

The Midterm Exam will be worth 15% of your overall course grade and will only cover content from Lectures 1-7. The Final Exam will be worth 30% of your overall course grade and will be cumulative.

Both exams will be held remotely (due to COVID concerns) and synchronously (i.e. you will have to take the exam at the specified time, unless you make prior arrangements with us).

Redemption Policy

The Final Exam will consist of two parts: a “Midterm” section and a “post-Midterm” section. If you do better on the “Midterm” section of the Final Exam than you did on the original Midterm Exam, your score on the “Midterm” section will replace your original Midterm Exam score. This lowers the stakes of the Midterm Exam and gives you two opportunities to demonstrate your understanding of the content from the first half of the course. This also means that you can miss the Midterm Exam for any reason and have the score be replaced by your score on the “Midterm” section of the Final Exam (though we do not recommend this). You must take the Final Exam to pass the course.

Policies ✏️


Here’s how we will compute your grade.

Homework40%drop lowest
Groupwork (Discussion worksheets)10%drop lowest
Weekly Surveys5%drop lowest
Midterm Exam15%see the Redemption Policy above
Final Exam30% 

See below for information on drops.

Late Policy, Slip Days, and Drops

Each student has five slip days to use throughout the quarter. A slip day can be used to extend the deadline of a homework assignment by 24 hours. You can use at most one slip day on any single homework assignment. Slip days can only be used for homework assignments.

Slip days are applied automatically at the end of the quarter, and you don’t need to ask in order to use one. It’s your responsibility to keep track of how many you have left. If you run out of slip days and submit a homework late, it may still be graded so that you’ll see what questions you missed, but the grade will be changed to a zero at the end of the quarter. If you use more than five slip days, we will count the first five late assignments, and any late assignments after that will get zero scores.

Slip days are designed to be a transparent and predictable source of leniency in deadlines. You can use a slip day if you are too busy to complete a homework on its original due date. But slips days are also meant for things like the internet going down at 11:58PM just as you go to submit your homework.

If you have something going on in your life that is impeding your ability to do your classwork on time, please reach out to us as soon as possible so we can work something out.

In addition to providing you with slip days, we will drop your lowest homework, groupwork, and survey. This gives you some additional flexibility for unforeseen circumstances.

Students on the waitlist or who join the class late are expected to keep up with the work and submit assignments by the deadlines.

The late policy will be strictly enforced out of fairness for all students.

Regrade Requests

You can ask for a regrade on any assignment if you believe that the grader made a mistake. Remember that clarity is a part of your score — if you had the right idea but were unable to clearly communicate it, you may still not deserve full credit. We ask that you please submit your regrade requests within one week of the assignment grade being released; you can submit regrade requests directly on Gradescope.

Incomplete Grades

Because of the pandemic, we must prepare for the unfortunate possibility that you will get sick and be unable to participate in this class for long periods of time. The university has a mechanism for helping in this situation: the Incomplete. If you are unable to complete the course because of reasons outside of your control, you may be given an Incomplete instead of a letter grade. This simply means that you will complete the rest of the work at a later time. Once you have done so, your overall grade is calculated and your Incomplete grade is replaced.

An Incomplete does not allow you to re-do work that has already been completed, only to do work that hasn’t been completed.

Unfortunately, your personal health is not the only thing that might prevent you from participating in this class. Some of us will get sick, others will have family members fall ill, and others might lose their jobs. If you have any doubt about your ability to perform satisfactorily in this course due to something outside of your control, please contact us ASAP and we’ll figure something out.

Academic Integrity

In this class, we expect that you will work hard, utilize allowed resources to master the course material, and act with integrity. Learning partially remotely presents new challenges for academic integrity, making it more important than ever to act honorably and make sure that the work you are submitting is reflective of your knowledge and abilities.

The UCSD Policy on Integrity of Scholarship and this syllabus list some of the standards by which you are expected to complete your academic work, but your good ethical judgment is also expected. Ignorance of the rules will not excuse you from any violations.

For this class, the following activities, among others, are considered cheating and are not allowed:

  • Sharing written homework solutions with other students, or viewing written homework solutions from another student.
  • Looking or asking for answers to homework problems in other texts or sources, including the internet.
  • Collaborating on exams.
  • Using unauthorized resources on homeworks or exams, including solutions from past iterations of this course.

The following activities are examples of things that are allowed in this class:

  • Discussing homework problems with classmates and the instructional staff.
  • Reading about concepts from lecture in outside texts, including the internet, without looking for answers to specific homework questions. If you accidentally find related material in another source, you must cite the source on your homework and write up your answer without consulting the source. To do otherwise is plagiarism.

Remember, Academic Integrity is about doing your part to act with honesty, trust, fairness, respect, responsibility and courage. If you are suspected of dishonest conduct, you will be reported to the Academic Integrity Office. Violations of the academic integrity policy will result in failing the course, and the Dean of your college may place you on academic probation or suspend or dismiss you from UCSD. Academic integrity violations are serious and the risk is not worth it!

A note on letter grades

The following is adapted from CSE 160 at the University of Washington.

Grading for this class is not curved in the sense that the average is set at (say) a B+ and half of the class must receive a grade lower than that. If everyone does well and shows mastery of the material, everyone can receive an A (this would be awesome!). If no one does well (this is unlikely), then everyone can receive a C.

Grading for this class is curved in the sense that we do not have a pre-defined mapping from homework and exam scores to a final GPA. There is no pre-determined score (e.g., 90% of all possible points) that earns an A or a B or a C or any other grade. To determine the final grade, we will ask questions like “Did this student master the material?”. With that said, grades will not be any stricter than the standard grading scale (where an A is a 93+, A- is 90+, etc). For instance, the threshold for an “A” will never be higher than 93%.

Try your best not to worry about them, and we’ll reciprocate by being fair. We’re in this together 😎.

Support 🫂


Students requesting accommodations for this course due to a disability or current functional limitation must provide a current Authorization for Accommodation (AFA) letter issued by the Office for Students with Disabilities (OSD). This AFA letter should be shared with the instructor and the Data Science OSD Liaison, who can be reached at Please contact us by the end of Week 3 to make sure we can arrange accommodations as needed.

Diversity and Inclusion

We are committed to creating an inclusive learning environment in which individual differences are respected and all students feel comfortable. If you have any suggestions as to how we could create a more inclusive setting, please let us know. We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community. Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and able to thrive.

Acknowledgements 🙏

This offering builds off of earlier offerings of DSC 40A by Janine Tiefenbruck, Justin Eldridge, Gal Mishne, Yian Ma, and Georgio Quer; they created the vast majority of the course materials we will use this quarter.