Grading Overview

Assignments (35%)

There are four assignments in this class. Assignments are to be completed individually by each student; you will not have a partner for any assignments. The first assignment serves as an introduction to the tooling used in the class, and the next three are designed to develop your skills in exploring data, asking interesting questions, and answering those questions using visualizations. Additionally, each assignment includes a written portion where you explain the questions you sought to answer with visualizations and the rationale behind your design decisions. Finally, each assignment asks you to provide constructive criticism on a professional visualization that you find in the wild. Some assignments have opportunities for extra credit.

• Assignment 1 (5%): Tutorial on web programming and D3.
• Assignment 2 (10%): Exploring a dataset and asking interesting questions.
• Assignment 3 (10%): Multiple datasets + coordinated views.
• Assignment 4 (10%): Interaction and animation.

Final Project (25%)

The final project for this course is to be completed in groups of two and will be due at the end of the semester on the final day of exams. You will identify a topic of interest and find one or more datasets that will be used to explore your chosen topic. The final submission is in the form of a webpage of visualizations and discussions on your topic, a presentation on the final day of class, and a written report detailing your process of finding and processing a dataset and creating your visualizations. The written report will be due at the end of the course’s final exam slot. You and your partner will propose a topic near the middle of the semester, and there will be progress updates throughout the remaining weeks of the semester that will contribute to your final project grade.

Pop Quizzes (15%)

There are short quizzes throughout the semester to assess your theoretical understanding of the course material. These quizzes are given in class and without prior announcement. New quizzes will include repeated (or slightly modified) questions from previous quizzes, so you are highly encouraged to correct your quizzes and check the answers with me or your classmates. There are no make-up pop quizzes, but your lowest quiz grade will be dropped at the end of the semester.

Midterm (15%)

There is an in-class, written exam on October 21. The exam will test your understanding of the theoretical concepts of data visualization and your programming skills with D3. More details will be shared closer to the exam date.

Class Participation & Engagement (10%)

Visualization design is an iterative and creative process that requires practice to get good at. To help you practice your visualization skills, we frequently hold discussions, case studies, and “lab” sessions during class. Bouncing ideas off of others and providing constructive criticism is a core element of these in-class exercises. Therefore, attendance is crucial not only for your learning but for your classmates’, too.

Your participation grade is based on your effort and engagement with the course. Things like showing up to class, volunteering to critique a visualization you like at the start of class, coming to office hours, doing quiz corrections, filling out feedback surveys, and putting in an honest effort to engage with the material will yield full credit for this portion of your grade. I recognize that not everyone will feel comfortable asking and answering questions in the public classroom setting, so you will not be graded simply on how often you raise your hand. But, we will have many in-class exercises and discussions which you are expected to earnestly participate in.

Textbooks

The following books provide good background information and greater detail on the topics covered in the lectures. The schedule points to specific sections of these books that you should read before the associated lecture. If a lecture assigns a reading not available in these textbooks, a link to the reading is provided in the schedule. The textbooks are available on CLIO.

• Visualization analysis and design by Tamara Munzner
• Information visualization: perception for design by Colin Ware

Data sources

For the assignments and final project, you will need to find one or more datasets to create visualizations of. Below are some helpful resources for finding datasets, but you are also free to look elsewhere for datasets.

• Data Visualization Society curated list
• NYC Open Data
• Kaggle Datasets
• Tableau Datasets

Late Submissions

Submissions are generally due at 11:59pm on their due date. A total of three late credits are given, which allow you to extend your submission deadline on an assignment by 24 hours each. You cannot use any late credits on submissions related to the final project.

A linear late penalty will be applied to late assignments, up to 3 days. For example, if your assignment is 12 hours late, you will receive a 16.66% penalty (12/72 = 0.1666). Late assignments due to illness or unexpected events can be excused with doctor’s notes or other appropriate forms of documentation.

Collaborations

Learning does not happen in a vacuum, and working with others is often one of the most effective ways to deepen your own understanding of a concept. While I am always ready to work with you, I also strongly encourage you to seek out your classmates and collaborate with them as you move through the course material. Note that quizzes must be completed solo, without any input from another person. However, it is a good idea to study for such a quiz by first working with another person or two. Students must include a written acknowledgment in their assignment submissions if they consulted anyone else about their work.

Copying analyses of a dataset from other people is not collaboration and is not allowed. You may browse the many visualization examples online (e.g., [1], [2], [3]) to get inspiration for your own visualization designs, but the questions you ask about a dataset and the subsequent work to create the visualizations to answer those questions must be your own.

Generative AI

The use of generative AI in this class is prohibited. Students must complete all assignments through their own critical thinking, analysis, and writing. The use of AI systems (including but not limited to ChatGPT, Claude, Gemini, or similar platforms) is not permitted for any phase of assigned work.

For many of you, this class may be your first experience with designing and creating visualizations. Therefore, taking the time to carefully work through the material (reading, taking notes, attending lectures, doing the assignments, studying for quizzes) is crucial for building the foundational skills necessary to become visualization experts. If you find yourself having difficulty with understanding the concepts covered in class or with finishing assignments on time, you should reach out to me (via office hours or email) so we can work out a plan to resolve these issues.

Student-Professor Partnership

To reach the goals of the course’s learning outcomes, we need to work together. I highly encourage you to ask questions in class where all students can benefit from hearing questions and answers. If further clarification is needed, please make use of my office hours–I am fascinated by the course material so it is fun for me to talk about it with you! Office hours are also a good time to talk over questions you have about computer science, graduate school, career options, etc. Don’t be surprised if we also get talking about what interests you outside of academics, because I enjoy getting to know each of my students. Should you wish to discuss study strategies for this course and/or others, please come by my office hours.

Finally, I realize that talking with professors can be intimidating at times, but making use of office hours to have such conversations helps you practice skills needed to interact as a young professional. In other words, it is another way to prepare for post-Barnard life.

Barnard Honor Code

You are expected to hold yourself to the highest standard of academic integrity and honesty, asreflected in the Barnard Honor Code. Approved by the student body in 1912 and updated in 2016, theCode states:

We, the students of Barnard College, resolve to uphold the honor of the College by engaging with integrity in all of our academic pursuits. We affirm that academic integrity is the honorable creation and presentation of our own work. We acknowledge that it is our responsibility to seek clarification of proper forms of collaboration and use of academic resources in all assignments or exams. We consider academic integrity to include the proper use and care for all print, electronic, or other academic resources. We will respect the rights of others to engage in pursuit of learning in order to uphold our commitment to honor. We pledge to do all that is in our power to create a spirit of honesty and honor for its own sake.