The purpose of extra credit (EC) is to reflect understanding of the course material more in-depth or from a different perspective. Viewed in another way, these are assignments that were not assigned this semester due to our limited class time, but are still lots of fun and worth your time.
Each EC assignment earns you up to 5% added to your final grade after any class curve. The amount you earn will depend on how in-depth you choose to go. Before you start working on one of the following, email me your proposal (what is your topic? how in depth do you plan to go?) and I’ll respond with my point expectation and rubric given your proposed scope.
When complete, submit via same Assignment Submission form (choose “Extra Credit” in the dropdown) by latest Monday 11/26, 11:59pm. (We will grade and award EC on rolling submission.)
Must specifically misrepresent two opposing/conflicting messages by utilizing one or more perceptive principles. You’re encouraged to treat this as a data art assignment; however, it must be based on real data (however simple) and it must illustrate at least one perceptive principle. Take for inspiration Josef Albers, MC Escher, optical illusions, etc. Submit as slides or PDF with your images and a paragraph caption.
Choose a published visualization (general media or academic) and redesign it to improve the communication. Include a writeup on your critiques of the original, and reasons behind your modifications. Your redesign should be structurally significant, and not merely adding a few labels or touch-ups. Consider: how might you redesign for a different audience? with a more thorough or more descriptive dataset? to prioritize a different message? to change the narrative structure? Make sure you read Design and Redesign in Data Visualization by Viegas & Wattenberg, 2015 before you start. You’re welcome to use any software tool you like.
Choose an algorithm to explain to the world, assuming your reader wishes to learn it for the first time. Consider an algorithm that lends itself well to being understood through visual presentation compared to how it’s traditionally explained. Make sure you read Visualizing Algorithms by Mike Bostock (2014) and Explorable Explanations by Bret Victor (2011) before you start. Build your explainer on Observable, or for the web.
Build a how-to tutorial as a general data visualization resource for your fellow students and the broader internet community. This should be relevant to a theme discussed in class, but go beyond what’s covered in lecture. Good tutorial topics include how to use D3 with specific data analysis techniques, a specific class of datasets, or other software packages. (E.g. if for one of your major assignments you spend a lot of time in one specific step, this is an opportunity to write it up, teach others your solution, and get extra credit.)