Design Project
Students will work on a project in teams of four people.
The idea of the project is to take the knowledge and background that you are learning this semester about data visualization and put it to good use in a new, creative effort. A real key to the project, however, is to select a data set that people will find interesting and intriguing. Even better would be to select a data set with a clearly identified set of “users,” “analysts”, or “consumers” who care deeply about that data. Select a topic that people want to know more about! I cannot emphasize strongly enough the importance of your topic and data set. No matter what topic you choose, I am expecting a high-quality project. In particular, I’m seeking creative projects showcasing interesting ideas.
A good project should consist of an effective visualization design and an implementation of the design using some visualization system. You are free to choose any visualization tool or tools that you want in order to help create your visualization. Since we are studying Tableau this term, it might be a good choice. Note that you can even use a combination of tools.
You will have several main milestones or deliverables. First, you must form your team. Second, you must select a topic and data set. Third, your team will have a midway project review with the professor and TAs, and it will focus on the design goals, intent, and motivation of the design, as well as early design drafts and mockups. Fourth, you will give a concluding explanation/demonstration of your project to the instructor and TAs at the end of the semester. Finally, you will create a video (3 minutes or less) that explains your visualizations and shows it in action. All teams will show their videos in our “Video Extravaganza” during the class’ final exam period. You will also complete a peer review for your teammates, and your assessment of their contribution will become part of their project grade.
Important Milestones
- Jan 31 – Team formation. Once you know your partners, you should list the four members of your team on the Canvas page for the Project. If you’re not set up on a team, email Prof. Stasko and he will set you up with other students.
- Feb 14 – Initial project description. 1-2 page document listing project members and describing topic to be addressed and data sources/formats. You should address the following questions:
- What is the problem being addressed?
- Where is the data coming from and what are its
characteristics? - Who would be interested in understanding this data better?
- What would these people want to know about the data?
- Mar 7 – Midterm Project Review. This milestone is an opportunity for you to discuss your project motivation, intent, and high-level goals in a meeting with the instructor and TAs. You should have all your data by this point. What type of visualization do you want to design, something for analysis, exploration, communication? If it’s more analytical, what types of tasks do you seek to support and what questions will your visualization help answer? If it’s more communicative, what do you want viewers to learn and what insights will it surface? Additionally, it is an opportunity for you to present your initial view designs, visualization choices, user interface plans, etc., in order to receive feedback about them. You should be creating a variety of design ideas for your visualization. Illustrate those design ideas through mock-ups, sketches, and storyboards, and bring those to this session. Ultimately, this midway project review is an opportunity for your team to get valuable feedback about the progress and direction of the project through a short meeting with the instructor and TAs (~15 minutes). Be prepared to answer many questions about your plans. You should prepare a brief initial overview presentation of where things stand (2 minutes) and your group will meet with the instructor on Mar 11 to receive preliminary feedback.
- Mar 26 – Project materials. Each team will submit a project overview file (one-page pdf) to a Canvas assignment. Include on this page your team number, team members, project topic, example screenshot of a project view, paragraph description of project, and (most importantly) the URL of the webpage where your project can be found. Even if you have just built a Tableau project, please embed that onto a Tableau Public webpage so that the project can be viewed from anywhere.
- Apr 11 – System video. Create a 5 minute or less video that describes the problem you addressed and that showcases your visualization. You will need to submit it through the Canvas assignment. Use mp4 format on the video at least 720p but not bigger than 1080p.
- April 15/17 – Project presentation/demonstration. Each team will show and explain their final visualization and describe what they have done towards the final weeks of class. Your presentation should include your topic, team member names, screenshot and walk-through of your visualizations, your problem description and key takeaways from the data you examined. Upload your slides onto CANVAS.
- April 18 – Peer Review due. Towards the end of the semester, your group members will fill out an evaluation form to share their perspective of your contribution to the project with regards to developing ideas, willingness to discuss with others, ability to cooperate with others, enthusiasm in the project, and effort accomplishing your share of the project. Your group member’s assessment of you will become part of your project grade.
Grading
The following questions will be important during that evaluation process.
- Does the system (ie, your visualizations) work, ie, does it read
in the data and present visualizations of the data? - Are the visualizations an effective representation of the data
going beyond what one could ascertain simply by looking at the data files? - Do the visualizations support different analytical questions
about the data and/or do they help the viewer better understand and
gain insights about the data? - Are the visualization design choies appropriate and do they
follow good datavis design principles? - Do your visualizations exhibit some creativity or visual interest
beyond the simplest standard views? - Was your presentation/demonstration (final meeting) an effective
illustration of your project and work? - Does your video illustrate your system well? Does it
explain the problem and solution well enough so that a person
unfamiliar with the project can appreciate your contribution?
Besides peer review, the grade earned for the project will be a team grade, that is, all team members will earn the same score for the project by default. However,the professor reserves the right to adjust individual team member’s scores either upward or downward to support especially strong or weak performance and contributions to the group effort, as much as he can objectively determine. It is acknowledged that not all team members will bring the same skills to the group. It is each member’s responsibility, however, to make a significant contribution in whatever way that best matches his or her abilities.
Tips for a Successful Project
It is extremely important to select an interesting problem with data that some group of people will care deeply about. I cannot stress enough how vital it is to start with interesting data. Find some topic that almost everyone cares about (e.g., baby names, feature films) or that some subset of people really care about (e.g., sports data, politics). Find some topic that will draw interest such as sales of stocks by US Congress people or dark money and lobbying influences on Congress. Consider combining different data sets to produce a new composite data set of special interest. Such a fusion of data often creates a data set that people want to learn about. You may need to take a few weeks of the semester where you simply focus on data acquisition. That’s OK. If you do not have your data nailed down by midway project 1 then you will be in deep trouble.
Examples
To see examples of some nice projects from the past, we have compiled links to top projects from the
course sections.
Additionally, you can find interesting projects on the web where someone did a deep dive visualization project on the data surrounding some topic.
Examples include the Seinfeld TV
show, movies, and the
Marvel
Cinematic Universe. We’ll see many more examples in class too.
Data
Where do you get your data from? Be creative! You may have to scour the web and scrape some information. You may have to manually log and generate data tables. You may have to coalesce multiple different data sources. Part of your responsibility on the project is to come up with the data needed to drive your visualization. It is a crucial and vital initial step! Ideally, you should start with a problem or domain, find someone who knows more about it, and then look for data from there. Don’t start your project simply by looking for any old dataset. Be more problem-driven than that. Below are some quick examples of places with nice data repositories.
- OECD World Statistics
- World Bank Open Data
- ProPublica Data Store
- BuzzFeedNews Data Store
- Opportunity
Insights data - World
Bank data by indicators - RockContent
open data sources - Awesome
public datasets - 538.com’s data
- Data
sets from Storytelling with Data - Data sets from
data.world - Atlanta Open Data
& Mapping Hub - Big
list of data sets - More data sets
- FEC Campaign Finance Data
- US Senate and House stock trades,
- CS faculty
- John’s
16spring VDE class data sets
There have been many datasets related to Covid-19 created. Below are just a few.
Also, consider organizations that run contests for developing visualizations. They can often serve as good sources of problems and data. Some examples are:
And finally, there are websites that can help you find data sets such
as Kaggle
and Google’s
Dataset Search Tool.