Assignments and grading
Grades
Course grades will be determined by each student’s score in the five categories listed below. Each category contributes a specific number of points to the total grade:
48 Possible Homework points
6 Possible peer-review points
20 Possible participation points
2 Possible award points
24 Possible project points
Total: 100 possible points
Expected grade breakdown (subject to change!):
- A: 86+
- A-: 82-85
- B+: 78-81
- B: 74-77
- B-: 70-73
- C+: 66-69
- C: 62-65
- C-: 58-61
- D: 50-57
- F: < 50
Homework
Submission
Students should submit all assignments on canvas before the listed deadline.
Grading and feedback
Students are responsible for presenting each assignment to the instructor in-person (or on zoom) for grading and feedback. Opportunities will be available during office hours and during dedicated lecture times. Students who do not meet with the instructor will not receive feedback and may not have opportunities for re-submission.
Re-submission
Any assignment may be revised and re-submitted before the end of the semester for a higher grade. Note that this will not erase any late penalty applied to the assignment. The initial submission must still be received before the stated deadline.
Submission Format
All submissions to Canvas must be either HTML or PDF files. No other formats will be allowed.
HTML files must be self-contained and submitted as a single page (see the course Quarto template for rendering a single-file site). Custom backends (Node.js, Python, etc) and database systems, such as Postgres or MySQL, are not allowed. Note that this includes Jupyter widgets and Shiny/Streamlit apps as they rely on a hosted server. All interactivity must be implemented directly in browser
Note that external web resources such as CSS frameworks, Javascript libraries and calls to 3rd-party APIs are allowed, as long as they can be dynamically-loaded as part of an HTML page.
There are no restrictions on what tools you use to implement your submissions; you are not required to use Altair/D3. You may use other Python or Javascript libraries, graphic design tools or even hand-drawing for any submission as long as it meets the specified requirements.
Note that the instructor may not be able to assist with other tools!
Any violations of the submission format or any submissions that require modification by the instructor to get working will incur a 1-point penalty on homework or a 4-point penalty on the final project.
Homework Template
A Quarto template is provided for homework assignments. By default this template will render homework to a single-page, website using a single HTML file that can be submitted to Canvas. It is recommended, but not required to use this template for most homeworks.
The template is available here: .
Possible homework grades
Meets requirements - 2 Points
The submission meets the stated minimum requirements for the assignment, but could be improved in one or more of the following ways:
Polish: Visualization is difficult to read, incomplete or little care was put into the aesthetics.
Design choices: Visualization makes sub-optimal choices for effectiveness, expressiveness or other metrics, leading to a difficult to interpret visualization.
Insight: The visualization does not provide enough insight into the data, and/or the choice of data provides little opportunity for new insight.
Creativity: The visualization is very straightforward and doesn’t leave a lasting impact.
Solid submission - 3 Points
The submission meets all the requirements and is a polished, well-thought-out and insightful visualization according to all of the evaluation criteria. This visualization could be included within formal writing or academic work.
Outstanding submission - 4 Points
The submission goes above and beyond the requirements for the assignment and produces a headline-worthy visualization. This submission should excel in one or more of the following metrics:
Creativity: The visualization includes creative choices that make for a novel view of the data or that push the boundaries of visualization in some way, encouraging the reader to think differently.
Impact: The visualization makes strong use of artistic or design elements that reinforce the central message of the visualization, while maintaining the principals of good visualization.
Insight: The visualization explores a particularly challenging dataset, presents unexpected results or incorporates insightful statistical analysis to augment what can be seen in the visualization.
Engineering: The visualization required significant engineering effort to create, most notably in creating polished and complex interactions/animations.
Improvement: The visualization has gone through one or more rounds of revision incorporating peer feedback to create an exceptionally polished and well thought out visualization.
Note: As the outstanding submission grade is meant to represent a publication-ready or showcase submission, It is expected that attaining it will require one or more rounds of editing revision and revision after the initial submission. Before submitting work with the goal of achieving this grade, you should consult with the professor and/or your peers to ensure you have a solid plan.
Late submission - -1 Points
Submissions received after the deadline are will receive a standard deduction of 1 point from whatever grade would otherwise be assigned.
Homework Collaboration Policy
Homeworks are individual assignments, and thus the code used for data processing and visualization in each submission must be your own. You should aim to use different datasets than your peers for each assignment or at least different variables within each dataset.
You are strongly encouraged to form study groups as well as give feedback and technical assistance to your peers for all homework assignments.
To summarize, under the collaboration policy you may:
Freely discuss course materials, topics and assignments
Share helpful resources and examples with other students
View another student’s code for inspiration or to help them debug technical issues.
Give feedback on incomplete submissions.
You may not:
Directly copy and paste another student’s code.
Replicate another student’s submission by visualizing the same data in the same or similar way.
Peer-review
Each homework assignment will have a corresponding peer review. You will be assigned to review one other student’s submission (anonymously and at random). Peer reviews are graded only for completion, but remember that good peer reviews are valuable to your classmates. Consistently excellent peer reviewers will be eligible for bonus points from the outstanding peer reviewer award.
When writing peer reviews, you aim should be to help your peers improve their visualizations to be as good as possible. Imagine that you are a visualization editor working to prepare the work for publication.
Make sure to highlight the positives as you go, what did you like about the visualization? What did it teach you? What did you find especially fun or creative?
Point out anything that you found confusing, unclear or difficult to read. Making sure that a visualization has the intended message is one of the most valuable goals of peer review.
Keep our evaluation criteria in mind as you critique the visualization. It can be helpful to point to specific metrics that can be improved.
Give concrete suggestions for improvement, e.g. should they consider a different colormap/geometry/etc.? Add more artistic flair? Include summary statistics? This can make iterating on the visualization much easier.
Consider how well the visualization(s) met the stated criteria for the assignment.
Peer reviews should generally be at least a paragraph in length in order to touch on the points above, but may be longer at the review’s discretion.
For assignments that don’t directly focus on creating visualizations you should still focus on what was interesting, insightful and useful from the submission, along with how well the submission met the stated assignment criteria. Try to look at the assignment from the instructors perspective and judge how well the submission met the spirit of the assignment and what could be done to improve it.
Submission: Peer-reviews should be submitted through Canvas on the corresponding assignment page.
Participation
In-class exercises
In-class assignments are graded for completion only. Participation grade is determined by the number of in-class assignments attempted with a good-faith effort. If you miss a class session, you may receive credit by completing the exercise on your own and showing the instructor.
Note that different weeks may have considerably different amounts of in-class work and not every week will have in-class work. If there are no in-class assignments and you miss class, you should notify the professor (or Grutor) and confirm that you completed the assigned reading(s). The instructor reserves the right to confirm this with comprehension questions. The participation grade will be determined by-week rather than by assignment, so completing in-class work for 12 out of 15 weeks would result in an 80% participation grade (16/20 points).
Attendance
Attendance is not strictly mandatory, however students are still responsible for demonstrating completion of all in-class exercises, regardless of classes missed. In-class exercises may be present even on days marked for lecture.
End-of-semester awards
At the end of the semester will be eligible for up to 2 points from awards recognizing outstanding work in the class. Awards will translate to points as follows:
Winner (or co-winner): 2 points towards the final grade
Honorable mention: 1 point towards the final grade
Outstanding visualization award
This award is presented to the best overall visualization submitted over the course of the semester. Winners of this award should be uniformly excellent over all of the visualization criteria discussed.
Outstanding innovation award
This award is presented for the most creative, artistic or unique visualization submitted over the course of the semester. Winners of this award should bring something new and unexpected, while still showing excellence along the other metrics of evaluation.
Outstanding peer-reviewer award
This award is presented to the student(s) who submits the most useful, in-depth and well-written peer reviews over the course of the semester.
Project
Please see the course project page for details on the course project!