CS 448B Visualization

Stanford CS course on data visualization techniques (Fall 2023)

Location: Thornton Center Rm 102
Time: MW 11:30am-12:50pm


Final Project

Project Proposal Due: Monday Nov 06, 2023 by 11:30am
Design Review and Feedback: 9th week of quarter
Final Code and Video Due: Sunday Dec 10, 2023 by 8:00pm

Purpose

The purpose of the final project is to provide hands-on experience analyzing a data set and producing a visual explainer style article for it. More specifically a data analysis and explainer project involves analyzing a data set that has not previously been analyzed extensively, and producing an interactive visual explainer style article of the most interesting findings you uncover in the data set (e.g., an article like those found at pudding.cool or the New York Times).

Projects will be carried out by a team of up to 3 people. The final implementation deliverable is interactive article containing at least three substantially different interactive charts with accompanying text written in the form of a journalistic article and a short video demo (up to 2 min long with voiceover) walking through the article.

The first step is for you to identify the specific topic you will work on. We would like you to write this up as a project proposal which is due on Monday Nov 06, 2023 by 11:30am. Each group will then be responsible for presenting the project in draft form to the teaching staff in the 9th week of the quarter. This design review will give you a chance to obtain feedback on the work and prepare for the final deliverables which are due on Sunday Dec 10, 2023 by 8:00pm.

Suggested Project Topics

To get you started in thinking about project ideas we have posted a few final project suggestions for you to consider.

Deliverables/Schedule

Project Proposal Due: Monday Nov 06, 2023 by 11:30am

As a first step you should create a project proposal that includes the names of the members of your group and a short (about 2 paragraph) description of the data analysis/explainer you plan to work on. You should think about the dataset and the types of interactivity you plan to support in your explainer. You should submit the proposal via Canvas.

Upload your final project proposal to Canvas.

Design Review and Feedback (9th week of qtr)

In the 9th week of the quarter we (the teaching staff) will review your project to provide feedback on the project and help you prepare for the final submission. It is fine if your project is not yet in a fully “complete” state, but by this point you should have made substantive progress, including working (if still rough) prototypes of your main visualizations and interactions.

For this review you should prepare a short presentation of your work focusing on a demo of what you have working so far. More information on the timing of these reviews will be posted as we get closer to the 9th week.

Final Project Code and Video Due: Sunday Dec 10, 2023 by 8:00pm

The final deliverables include:

  • Code: an implementation of your project. Please also post a link to a running version of your site. If you have questions about how to serve your site please talk to us right away.

  • Video: a 2 minute video demo (with voiceover) explaining your project. The video should demo the interactions in the visualizations and describe your main findings.

Submission: You should submit your final deliverables (access to a running executable and a zip file of the code, or a link to a github repository as well as the video) via Canvas.

Resources

Guidelines for a Successful Final Project

  • Consider the Audience: Your visual explainer should be of interest to a group of people beyond your immediate circle. An explainer about your music-listening or messaging habits is unlikely to be of interest to others you don’t know.

  • Pick a Unique Topic/Dataset: You should create a visual explainer on a topic or dataset(s) that have been relatively unexplored. It’s important and helpful to do some research on what has already been done for the topic/dataset(s). Certain data like songs (e.g. Spotify) or movies (e.g. IMDB) are already well analyzed and should be avoided, unless you want to try to take a very different angle or use innovative analysis methods.

  • Develop a Narrative: In the early stages of the analysis process, try to uncover patterns to help you form and shape a narrative through-line for the explainer. Having a clear narrative can help you scope your analysis work and can make the explainer more engaging to the reader.

  • Design Visualization Interactions: You should choose base visualizations that can support a high level of interactivity. Bubble charts, tree maps, and word clouds typically aren’t the most effective choices. You should design interactive features that would enable viewers to interact with the data in a way that strengthens your narrative. Simply supporting a tooltip is typically not enough interaction. You can draw inspiration from sites like the New York Times, the Pudding, and various others that have been showcased in class.