Welcome to DSC 140B in Winter 2026! This page should answer most of the questions you might have about how the course is run; check out the frequently asked questions for answers to some common ones. If you don't find what you're looking for here, feel free to make a post on Campuswire.
This quarter DSC 140B is being taught by:
Feel free to send me an email, though for class-related questions you'll usually get a faster response if you ask on Campuswire (as then the TAs are also able to help out).
To get started in DSC 140B, you'll need to set up accounts on a couple of websites.
We'll be using Campuswire as our course message
board. You should have received an invitation via email, but if not you should
be able to join by clicking the link above and using the access code 8590. Be sure to join Campuswire as soon as possible,
since all course communication will be done through it.
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. Most of the assignments will be a mixture of math and coding, and
the coding parts are usually autograded via Gradescope. You should have
received an email invitation for Gradescope, but if not you can join with code
N2YKZY.
We will not be using Canvas. All course materials will be available at dsc140b.com or Gradescope.
You will not need to purchase any materials for this course; we'll use lecture slides as the main resource.
Lectures will be held in-person at the regularly-scheduled time and place: 11:00 AM on Tuesday and Thursday in WLH 2005.
Lecture attendance is optional, but attending lectures can earn you credit. See the Lecture Attendance section below for more information.
Lectures will be podcasted and posted online for remote viewing. You will be able to find the lecture recordings at podcast.ucsd.edu.
Discussions will be used for the optional quizzes (see below). They will be held in-person at the regularly-scheduled time and place: 8:00 PM on Thursday in WLH 2005.
The first half of each discussion section will be used for the quiz, and the second half will be used to go over the answers to the quiz and any questions you might have about the material. Since quizzes are optional, so is attending discussion sections.
Course staff, including tutors, TAs, and instructors, will hold office hours regularly throughout the week. Please see the office hours page for the schedule and for instructions.
Your grade is made up of several things in this course:
All but the last two components are optional. You can choose to complete as many or as few of the optional assignments as you like, in whatever combination you like.
Here's how it works: by default, your course grade is based entirely on exams (90% of your grade) and the final project (10% of your grade). By completing optional assignments, however, you can earn "credits" that reduce the weight of the exams. Each credit earned is worth 1% towards your overall grade and reduces the weight of the exams by 1%. The final project remains worth 10%, no matter how many credits you have. There will be a total of 40 credits available throughout the quarter.
That is, your grade will be calculated as follows:
$$ \begin{align} \text{Overall Grade} & = (\text{Exam Average} * (90% - \# \text{ of Credits Earned})) \\ & + (\text{Final Project} * 10%) \\ & + (\# \text{ of Credits Earned} * 1%) \end{align} $$
Example 1: You earned an average of 80% on the exams and got 100% on the Final Project, but you didn't complete any optional assignments and so you earned no credits. Your overall grade would then be \((80\% \times 0.9) + (100\% \times 0.1) = 82\%\), earning you a B-.
Example 2: You earned an average of 80% on the exams and got 100% on the Final Project, and you also completed the maximum number of optional assignments and earned all 40 credits. Your credits reduce the weight of the exams from 90% to 50%, and your overall grade would then be \((80\% \times 0.5) + (100\% \times 0.1) + (40 \times 1\%) = 90\%\), enough for an A-.
Here is how you earn credits:
Homeworks: each homework problem completed is worth a different number of credits (the exact value will be noted on the problem itself). The total value of all homework problems will add up to 24 credits, distributed across 8 homeworks. Homework problems are graded on effort, not on correctness, so you can earn full credit even if you get the problem wrong. On the other hand, you can also get zero credit for a problem even if you got it right, if your solution does not show sufficient effort.
Quizzes: each quiz you pass earns you 1.5 credits. There will be 8 quizzes throughout the quarter, for a total of 12 possible credits. Note that quizzes are pass/fail, with a score of 70% or higher being a passing score. If you pass, you earn 1.5 credits; but if you do not pass, you earn zero credits.
Attendance: each lecture you attend (except for the first lecture and the midterms) earns you 0.25 credits, to a maximum of 4 credits total. Note that there are 17 credit-earning lectures throughout the quarter, but attending 16 will get you the full 4 credits. Attending the additional lecture will not earn you any more credit above the maximum.
For more about these assignments, see the sections below.
This grading system allows you to "choose your own adventure" in terms of how you want to be graded. Here are a few examples that illustrate how the system works.
You feel confident in your exam-taking abilities, so you decide to skip all of the optional assignments and focus entirely on studying for the exams and doing well on the final project. By choosing to not do any optional assignments, you earn zero credits, and your exams are worth 90% of your overall grade. For instance, if you score an 85% on Midterm 1, a 75% on Midterm 2, and a 100% on the final project, your exam average is 80%, and your overall grade will be \((80\% \times 0.9) + (100\% \times 0.1) = 82\%\) (a B-).
Now let's say you decide to go all-in on the optional assignments, doing all of the homeworks, taking all of the quizzes, and attending all of the lectures. This will earn you the full 40 credits available throughout the quarter. You scored an average of 80% on the two midterms and got 100% on the final project. Since you earned all 40 credits, your exams are only worth 50% of your overall grade (\(90\% - 40\%\)). Your overall grade is then \((80\% \times 0.5) + (100\% \times 0.1) + (40 \times 1\%) = 90\%\) (an A-).
Let's say you get bored easily while doing the homeworks, and you only do the first problem on each of the 8 homeworks. That will earn you 6 credits in total throughout the quarter (\(8 \times 0.75 = 6\)). If you receive a 70% on Midterm 01, a 90% on Midterm 02 (exam average 80%), and a 100% on the final project, your exams are worth 84% of your overall grade (\(90\% - 6\%\)). Your overall grade is then \((80\% \times 0.84) + (100\% \times 0.1) + (6 \times 1\%) = 83.2\%\) (a B).
You took all of the quizzes, but unfortunately you didn't do so well and didn't pass any of them. However, you studied hard for the exams and scored 100% on both, as well as 100% on the final project. Your overall exam score is 100%, and since you earned 0 credits from quizzes, exams are worth 90% of your overall grade. This means that your overall grade is \((100\% \times 0.9) + (100\% \times 0.1) = 100\%\). Note that failing the quizzes didn't hurt your grade at all; it just meant that you had to rely on your exam scores and final project for your overall grade.
There will be two midterm exams:
The exams will be held in-person during the regularly-scheduled lecture times.
The final exam for DSC 140B is a "no fault" final split into two sections:
If your score on the midterm redemption section is higher than your score on the original midterm, it will replace that grade. Getting a lower score on a redemption section cannot hurt you (but it will make us sad). As a consequence, the redemption sections are effectively optional.
Under this policy, a bad performance on an earlier exam can be erased by good performance on the same material in a later exam.
Example: You got an "F" on Midterm 1 and a "B" on Midterm 2. You decide to take only the first redemption section on the final (though you could have taken both), and you receive an "A". Your midterm scores are now "A" and "B".
The redemption exams will be held on the date scheduled by the registrar: Thursday, March 19.
There will be a final project due at the end of the quarter during finals week. The final project is a mandatory assignment that is worth 10% of your overall grade, regardless of how many optional assignments you complete.
More details about the final project will be provided later in the quarter, but it will involve building a neural network "from scratch" in Python so that you can get hands-on experience with how neural networks work under the hood.
As mentioned above, homeworks are optional. Each homework problem you complete earns a different number of credits since some problems are more involved than others (the precise value is noted on the problem itself). The total value of all homework problems will be 24 credits. These problems will be distributed across 8 homeworks throughout the quarter. This means that, on average, each homework will be worth 3 credits (but some will be worth more than others).
Each homework will be due via Gradescope at 11:59 PM on the Wednesday after it is assigned except otherwise noted, and you'll have roughly a week to complete each assignment from the time it is posted.
Homework problems are graded pass/fail based on effort, not on correctness. This means that you can earn a credit for a problem even if your answer is wrong, as long as your solution shows sufficient effort. On the other hand, you can also get zero credit for a problem even if you got it right, if your solution does not show sufficient effort.
If you do a homework, you must write your solutions by hand unless the question explicitly says otherwise (a tablet is fine, and you can screenshot the problem statement into your solutions if you'd like). Typed or computer-generated solutions (e.g., via LaTeX) will not be accepted unless you have an approved accommodation from OSD.
Why this return to the stone ages? You may have heard of a new trend in education called "ChatGPT". ChatGPT (and similar AI tools) are extremely good at generating typed solutions to homework problems, so if we allowed typed solutions, it would be all too easy to copy-and-paste AI answers. You can still use AI to help you with the problems (we don't necessarily recommend it, but we can't stop you). At the very least, you might learn something by writing out the solutions yourself!
Some problems will be coding problems that require you to write code in Python. In these cases, the problem will clearly state that typed code is acceptable (we won't make you handwrite Python code!)
While you are encouraged to discuss homework problems with your classmates, all work that you turn in must be in your own words and in your own writing. The same goes for ChatGPT: using AI to help you understand a problem is fine, but you'll need to write the answer in your own words to earn credit.
Keep in mind that homework problems are graded on effort, not correctness. We made this change in order to remove 99% of the incentive to use AI to do your homework for you. If you put in sincere effort to solve the problems yourself, you'll earn full credit even if your answers are wrong. If you copy answers from AI, however, you run the risk of getting zero credit if the solutions don't show sufficient effort.
Quizzes are short, multiple-choice assessments that cover the material from recent lectures. There will be a total of 8 quizzes throughout the quarter. Each quiz you pass earns you 1.5 credits towards your overall grade. A passing score is 70% or higher. If you pass the quiz (even with a score of 70%), you earn the full 1.5 credits. If you do not pass a quiz, you earn zero credits for it, but it does not count against you in any way.
Quizzes are held on-paper and in-person during the first 30 minutes of each discussion sections (in the second part of the discussion, you'll go over the answers to the quizzes). Each quiz covers material from the Tuesday lecture and from the previous Thursday's lecture (and not content from the lecture earlier that day).
Why subject yourself to quizzes? First, they can only help your grade, so there's no downside to taking them. Second, they provide a good opportunity to review the material from recent lectures and help you maintain a steady study pace throughout the quarter.
Lecture attendance is optional, but attending lectures can earn you up to 4 credits towards your overall grade. Each lecture you attend (except for the first lecture and the midterms) earns you 0.25 credits, to a maximum of 4 credits total. Note that there are 17 credit-earning lectures throughout the quarter, but attending 16 will get you the full 4 credits. Attending the additional lecture will not earn you any more credit above the maximum.
Lecture attendance will be tracked via participation in the in-class discussion questions. Getting the questions right is not necessary to earn attendance credit; you just need to submit an answer.
You have four slip days to use throughout the quarter on any homework or the Final Project. A slip day extends the deadline by 24 hours. Slip days cannot be "stacked" or "combined" to extend the deadline further — the latest any assignment can be submitted is 24 hours after the deadline. Slip days are applied automatically at the end of the quarter, but it's your responsibility to keep track of how many you have left.
Once your slip days run out, late homeworks will not be accepted for credit. This means that if you submit a homework even one minute after the deadline and you have no slip days left, you won't be able to receive credits for it.
And remember: homeworks are optional! If you're having a busy week and can't get a homework done on time, it's perfectly fine to skip it. You won't earn a credit for that homework, but you also won't be penalized in any way. Essentially, the exams take the place of that homework in your overall grade.
If you're sick or have an emergency and cannot attend a midterm exam, please let us know! If you think you'll be able to take a make-up exam the day after the original exam, we can usually accommodate that (with a doctor's note or similar). However, we try to release grades and solutions for the exam as soon as we can after the exam date, so if you need a make-up exam later than the day after, we'll opt to use the redemption exam on the final as your make-up instead.
If you have a final exam conflict with another class, we can usually arrange to have you take the exam at a different time on the same day. Please let us know within the first three weeks of the quarter so we can make arrangements. If you let us know later than that, we may not be able to accommodate you.
As instructors, our job is to foster an environment where everyone, regardless of identity, feels welcome and is able to focus on learning. If there is something we can do in this mission, or if there is something preventing you from succeeding in the class, please let us know. If you feel uncomfortable speaking with us or are searching for help on a specific concern, there are several campus resources available to you, including:
More generally, if you have any concerns about your ability to focus or succeed in this course, or just need someone to talk to, please contact us ASAP and we'll figure something out.
If you have exam accommodations from the OSD, you should receive an email from the data science program that will ask you to provide your availability for your accommodated exam. The program will then schedule the exam and notify the instructor of its time and location. If you do not receive such an email by the end of the second week of classes, please let us know!
Please be sure to respond to the email from the data science program; if the program does not hear back from you, they will be unable to schedule your accommodated exam.
In a typical quarter, the midterm redemption policy has the same effect as a traditional "curve", therefore replacing the need for one. The standard grading scale (where an A is 93+, A- is 90+, B+ is 87+, etc.) will be used as a starting point, but once all scores are in, we will run a clustering algorithm to automatically find the best cutoffs for each letter grade. These cutoffs can only be lowered. For instance, the threshold for an "A" will never be higher than 93%.
We're currently limited by classroom size (there are no more physical seats left in the classroom), so I don't anticipate the class growing any larger this quarter. You might be able to make it in if someone drops, but it is hard for me to estimate how likely that is to happen.
If you are waitlisted, you can still join all of the course websites and complete all assignments by following the instructions in the "Getting Started" section above. If you do get off the waitlist later in the quarter, any work you did while waitlisted will count towards your grade. If you didn't complete any work while waitlisted, that's okay too; all of the assignments due before the drop deadline are optional!