Design Project
This document describes the capstone project for the course.
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 six main milestones or deliverables. First, you must
form your team. Second, you must select a topic and data set.
Third, your team will have the first of two midway project reviews
with the professor and TAs, and it will focus on the design goals,
intent, and motivation of the design. Fourth, the second of the two
midway project reviews, will focus on view design drafts and early
mock-ups. Fifth, 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.
Important Milestones
Sep 14 - 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.
Sep 28 - 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?
Make sure to describe the data and its attributes in detail. Show a
snippet from your data. I do
encourage teams to run their ideas by Prof. Stasko before turning in
this milestone. We will get initial feedback to teams by the
following Tuesday. In some cases, we will recommend changing
topic. If that occurs, we encourage the team to develop a new topic
as soon as possible.
Week of Oct 16-20 - Midterm Project Review 1. 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? 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 (3-4 minutes)
and then the session will be dynamic after that, with much Q & A.
Week of Nov. 6-10 - Midterm Project Review 2.
This milestone is an opportunity for you to present your initial
view designs, visualization choices, user interface plans, etc., in
order to receive feedback about them. Again, it will involve a
meeting with the instructor and TAs. At this point of the project,
you should have solid goals & motivations for the project, and you should be
developing a plan for what you will implement. 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. This midway project
review is an opportunity for your team to get valuable feedback
about the progress and direction of the project. Be prepared to
answer many questions about your plans. You should prepare a brief
initial overview presentation of where things stand (3-4 minutes)
and then the session will be dynamic after that, with much Q & A.
Dec 6 - 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.
Dec 6 - System video. Create a 3 minute or less video
that describes the problem you addressed and that showcases your
visualization. We will schedule about a 2-3 hour session, a "Video
Showcase" where each team presents its video and has a few minutes
available to answer questions about it. You will need to provide a
link to the video file to the instructor by midnight on Dec 6th, the day
before. Do NOT
email the actual large video file--Instead, submit it through a
Canvas assignment. Use mp4 format on the video at least 720p but
not bigger than 1080p.
Dec 7 2:45pm-5:00pm - Project video showcase. We will meet
in our normal classroom to view all of the different project
videos. Students will be able to ask questions to the members of
different teams about their projects.
Dec 7-8 - Project presentation/demonstration. Each team
will meet with the instructor and TAs to show and explain their
final visualization and describe what they have done. You will need
to prepare a one-page project overview document which you will both
submit through Canvas and bring four copies to the meeting. The
document should include the following items: topic, team
member names, screen shot, problem description & project overview
(1 paragraph).
Grading
Once your topic has been chosen, each team is required to meet 1-1
with one of the TAs or the Professor at least once. This can be a very
valuable opportunity for you to get feedback about your design and
implementation. Failure to follow this requirement will result in
points being deducted from your project score.
To determine your grade for the project, we will evaluate the overall
quality of your project, including all milestones and components. It
is important that you make progress on the project quickly. This is
doubly beneficial as it allows more time for your implementation.
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?
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.
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.
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.