📖 Syllabus

About 🧐
Getting Started 💻
1. Websites
2. Development Environment
Communication 💬
Course Components 🍎
Policies 💯
Collaboration Policy and Academic Integrity 🤝
Support 🫂
Acknowledgements 🙏

About 🧐

Data visualization is one of our most powerful cognitive tools to make sense of large amounts of data. Well-designed visualizations enable us to use visual processing rather than calculation, allowing to “see” patterns in data. DSC 106 is a project-centered course that covers fundamental principles and techniques in data visualization. By the end of the course, you will:

Understand and apply key visualization techniques and theory.
Design, evaluate, and critique visualization designs.
Implement interactive data visualizations for the web using D3.js.
Develop a substantial visualization project.

Prerequisites: DSC 80.

Getting Started 💻

The course website, dsc106.com, will contain links to all course content. There are also a few things you’ll need to do to get set up.

Websites

You’ll need to make accounts or join the class on the following sites.

Ed: We’ll be using Ed as our course message and discussion board. More details are in the Communication section below.
⚠️It will take me a week or two to get access to my .ucsd email. Thus, we won’t sign up for Ed until then, so hang tight. If you have any questions in the meantime, email me at [email protected]⚠️
GitHub: Like in DSC 30 and 80, you’ll access all course content (lecture slides and assignments) by pulling our course GitHub repository. That repo is here: https://github.com/dsc-courses/dsc106-sp24. In most assignments, you won’t need to push anything to GitHub.
Zoom (optional): To access remote office hours, if you’d like.

Note that we will not be using Canvas for anything this quarter.

Development Environment

As soon as you are able to, go follow the steps in the Tech Support page of the course website to set up your development environment for the course.

Communication 💬

This quarter, we’ll be using Ed as our course message board. You will be added to Ed automatically; use the invite link in the section above if you weren’t added.

If you have a question about anything to do with the course — if you’re stuck on a problem, didn’t understand something from lecture, want clarification on course logistics, or just have a general question about data science — you can make a post on Ed. We only ask that if your question includes some or all of an answer (even if you’re not sure it’s right), please make your post private so that others cannot see it. You can also post anonymously to other students if you prefer.

Course staff will regularly check Ed and try to answer any questions that you have. You’re also encouraged to answer questions asked by other students. Explaining something is a great way to solidify your understanding of it!

Please don’t email individual staff members, just make a private or public Ed post instead.

Course Components 🍎

Lectures

Lectures will be held in-person on Tuesday and Thursday from 6:30-7:50PM in [SOLIS 104]. Lecture attendance is not strictly required. Lectures will be podcasted, and lecture slides will be posted before class.

Participation

Given the relatively late lecture time, lecture attendance will not be mandatory.

Discussions

We will not be using the scheduled discussion sections for this course. We encourage you to attend office hours instead.

Labs

There will be 7 lab assignments due weekly throughout the quarter. Each lab assignment is designed to be a walkthrough of practical material that you will use for your projects. Labs are meant to serve as worked examples: the solution code will be given to you as part of the lab. To receive credit for labs, you will need to complete the lab and then get checked off during office hours by a TA. The check off requirements will be clearly described at the end of each lab assignment.

Each lab is worth 2% of your final grade, and the lowest lab grade will be dropped. Labs will be released on Fridays and due on the following Fridays at 11:59PM.

You will access labs by pulling the course GitHub repository.

Projects

There will be 4 projects due throughout the quarter. The projects are open-ended and are the primary focus of the course. As such, our standards for grading will be similar to the way portfolio pieces are evaluated for internship/research lab/job applications — we expect your best work! The first three projects are solo projects. For the final project, you may work in pairs.

Working in Pairs

You may work together on the final project (and only the final project!) with a partner. If you work with a partner, you are both required to actively contribute to all parts of the project. You must both be working on the assignment at the same time together, either physically or virtually on a Zoom call. You are encouraged to follow the pair programming model, in which you work on just a single computer and alternate who writes the code and who thinks about the problems at a high level.

In particular, you cannot split up the project and each work on separate parts independently.

If you work with a partner:

Only one partner needs to submit the project on Gradescope; this partner should add the other partner to their submission.
You and your partner will receive the same score on any submissions you make together.

If you are unhappy with your partnership (e.g., if your partner does not keep in touch, does not come prepared to work on the assignment, or does not seem to be engaged in the process), please first address your concerns to your partner, and try to agree on what should be done to make the partnership work well for both of you. If that approach is not successful, explain the issues to the instructors, who will work with you and your partner to improve the situation.

Note that you may not work with partners on lab assignments, however you’re encouraged to discuss all assignments with others at a conceptual level in office hours and study groups.

Office Hours

To get help on assignments and concepts, course staff will be hosting several office hours per week. The majority of our office hours are held in-person, though a few are held remotely – if you’re able to, you’re strongly encouraged to attend in-person. See the Calendar tab of the course website for the most up-to-date schedule and instructions.

Weekly Schedule

To summarize all of the events and deadlines, refer to this general weekly schedule (which is subject to change in any given week):

Policies 💯

Grading

Here is how we’ll compute your grade:

Component	Weight	Notes
Participation	0%	Attendance is not mandatory.
Labs	18%	3% per lab, 1 lowest lab dropped
Project 1	12%
Project 2	15%	10% for submission, 5% for peer review
Project 3	15%	10% for submission, 5% for peer review
Final Project	40%	3% for proposal, 7% prototype, 15% video, 15% final submission

Extra Credit

Each week, an extra credit opportunity worth 1% will be available. To earn this extra credit, you must email me a data visualization you saw during the previous week (it may be from a website, a newspaper, a photo of something you saw, whatever), along with an explanation of what you found interesting and effective about that particular visualization.

I will select for the most interesting piece of visualization each week and share it during the Tuesday lecture. The student who sent the selected visualization will receive 1% extra credit.

To be considered for each week’s extra credit, email me a visualization and explanation before that week’s Sunday at 11:59pm.

Late Policy, Slip Days, and Drops

All assignments must be submitted by 11:59PM San Diego time on the due date to be considered on time. You may turn them in as many times as you like before the deadline, and only the most recent submission will be graded, so it’s a good habit to submit early and often. If you make a submission after the deadline, your assignment will be counted as late.

You have 6 “slip days” to use throughout the quarter. A slip day extends the deadline of an assignment by 24 hours. The number of slip days you can use on an assignment depends on the kind of assignment:

On labs, you may use up to 1 slip day. Since labs are due on Fridays and submitted via email, you may get checked off on the following Monday after a lab deadline to use 1 slip day.
On projects, you may use up to 2 slip days. Projects will not be accepted more than 48 hours after the deadline. Note that you will not be able to use slip days on the Final Project Deliverables due on Saturday, June 8, 2024.

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 an assignment on its original due date (or if you forgot about it). But slip days are also meant for things like the internet going down at 11:58 PM just as you go to submit your assignment. Slip days are meant to be used in exceptional circumstances, so you probably should not need to use all 6, but 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.

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’ve run out of slip days and submit an assignment late, that assignment may still be graded, but you will receive a 0 on it when we calculate grades at the end of the quarter. However, in the event that you use all 6 days and submit another assignment late, we will allocate your slip days first to your projects (in chronological order), then to your labs (in chronological order), and then to other assignments. This is done to prevent you from receiving a 0 on, say, Project 4, if you’ve used all of your slip days on labs up until that point; in such a case, you’d instead receive a 0 on an earlier lab that isn’t weighted as much in your grade.

Regrade Requests

If you feel that there in an error in grading, you may submit a regrade request within two days of the grades being released. If you do not submit a regrade request within two days, your original grade will be final.

To submit a regrade request for a manually graded problem, make the regrade request directly on Gradescope. Note that part of your grade is clarity, so if your answer was mostly right but unclear you may still not be eligible for full credit.

Incompletes

In the unfortunate circumstance that you become sick, suffer a loss, or otherwise experience a significant setback that is outside of your control, you may be eligible for an Incomplete grade, which allows you to complete the rest of the work at a later time. If you are experiencing challenges due to circumstances outside your control, please contact me ASAP and we can discuss the best course of action. Note that an Incomplete does not allow you to re-do work that has already been completed, only to do work that hasn’t been completed, so it’s best to reach out right away.

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 project 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 97+, A is 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 grades, and we’ll reciprocate by being fair. We’re in this together 😎.

Collaboration Policy and Academic Integrity 🤝

DSC 106 is known for being a rigorous but rewarding course. While you will be challenged this quarter, we will be offering you plenty of support through office hours and Ed. Make good use of these resources, and you will be able to succeed in this course.

There is no excuse for cheating in this course. If you do cheat, we will enforce the UCSD Policy on Integrity of Scholarship. This means you will likely fail the course and the Dean of your college will put you on probation or suspend or dismiss you from UCSD. Students agree that by taking this course, their assignments may be submitted to third-party software to help detect plagiarism.

Why is academic integrity important?

Academic integrity is an issue that is pertinent to all students on campus. When students act unethically by copying someone’s work, taking an exam for someone else, plagiarizing, etc., these students are misrepresenting their academic abilities. This makes it impossible for instructors to give grades (and for the University to give degrees) that reflect student knowledge. This devalues the worth of a UCSD degree for all students, making it imperative for the the campus as a whole to enforce that all members of this community are honest and ethical. We want your degree to be meaningful and we want you to be proud to call yourself a graduate of UCSD!

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 (or asking us for advice) is also expected as we cannot list every behavior that is unethical or not in the spirit of academic integrity. Ignorance of the rules will not excuse you from any violations.

What counts as cheating?

In DSC 106, you can read books, surf the web, get inspiration from previous work or AI tools like ChatGPT, talk to your friends and the DSC 106 staff to get help understanding the concepts you need to know to complete your assignments. However, all code must be written by you (or, in the instance of projects, together with your partner).

The following activities are considered cheating and are not allowed (not an exhaustive list):

Using or submitting code acquired from other students (except from your pair programming partner during projects), the web, or any other resource not officially sanctioned by this course
Posting your code online, including on Ed, unless privately to instructors only
Having any other person complete any part of your assignment on your behalf
Completing an assignment on behalf of someone else
Providing code to any other student in the course
Splitting up project questions with your pair programming partner and each working on different questions

The following activities are examples of appropriate collaboration and are allowed in (not an exhaustive list):

(new) Using AI code generation tools, such as GitHub Copilot and ChatGPT, to help write your code
Discussing the general approach to solving labs or projects
Talking about problem-solving strategies or issues you ran into and how you solved them
Discussing the answers to exams with other students who have already taken the exam after the exam is complete
Using code provided in class, by the textbook or any other assigned reading or video, with attribution
Using Google to search for documentation.
Working together with other students on assignments without copying or sharing answers
Posting a question about your approach to a problem on Ed, without sharing your code

How can I be sure that my actions are NOT considered cheating?

The best way to avoid problems is by using your best judgement and remembering to act with Honesty, Trust, Fairness, Respect, Responsibility, and Courage. Here are some suggestions for completing your work:

Don’t look at or discuss the details of another student’s code for an assignment you are working on, and don’t let another student look at your code.
Don’t start with someone else’s code and make changes to it, or in any way share code with other students.
If you are talking to another student about an assignment, don’t take notes, and wait an hour afterward before you write any code.

Support 🫂

Accommodations

From the Office for Students with Disabilities (OSD):

OSD works with students with documented disabilities to review documentation and determine reasonable accommodations. Disabilities can occur in these areas: psychological, psychiatric, learning, attention, chronic health, physical, vision, hearing, and acquired brain injuries, and may occur at any time during a student’s college career. We encourage you to contact the OSD as soon as you become aware of a condition that is disabling so that we can work with you.

If you already have accommodations via OSD, please make sure that we receive your Authorization for Accommodation (AFA) letter by the end of Week 1 so that we can make arrangements for accommodations. Share your AFA letter with the instructor and the Data Science OSD Liaison, who can be reached at [email protected].

Diversity and Inclusion

We are committed to an inclusive learning environment that respects our diversity of perspectives, experiences, and identities. Our goal is to create a diverse and inclusive learning environment where all students feel comfortable and can thrive. 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 feels comfortable.

Campus Resources

If there is an issue you feel uncomfortable speaking with us or are searching for help on a specific concern, there are several campus resources available to you, including:

Acknowledgements 🙏

This course is adopted from Sam Liu’s DSC 106, Winter 2024 course (e.g. this course last quarter). His acknowledgements:

Materials for this course have been adapted from classes taught by Soohyun Nam Liao and Jared Wilber at UCSD, Jeffrey Heer and Leilani Battle at UW, Arvind Satyanarayan at MIT, and Maneesh Agrawala and Michael Bernstein at Stanford University.

The class draws heavily on materials and examples found online, and we try our best to give credit by linking to the original source. Please contact us if you find materials where credit is missing or that you would rather have removed.

Sunday	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday
		Lecture		Lecture
					Lab and Project due