A summary of some top projects from this 2021
This document describes the capstone project for the course.
Students will work on a project in teams of 2 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
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 five 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 midterm project review and critique with
the professor and TAs. Fourth, you
will give a short explanation/demonstration of your project to the
instructor and TAs
during final exam week. 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.
Mar 17 - Team formation. If you know your partner, the two
members of your team should list your names on the Canvas page for
the Project. If you don't have a partner, email Prof. Stasko and he
will pair you up with another student.
Mar 26 - 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
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.
- What is the problem being addressed?
- Where is the data coming from and what are its
- Who would be interested in understanding this data better?
- What would these people want to know about the data?
Week of Apr. 8-14 - Midterm Design Review & Critique. This
is an opportunity for you to show what you have accomplished so far
and to receive feedback about the project's progress. You should
create and show
a variety of design ideas for the data set and topic you have
chosen. This is an opportunity
for your team to be creative and come up with a variety of ideas.
Be ready to answer questions about the purpose of the visualization
system and who the users will be. Provide a
list of analytic questions and queries that a person should be able
to answer using this visualization. Finally, you should explain
your plan for creating the visualization and how you will approach
that. This milestone may involve a short report and/or a short
meeting with the instructor and TAs. More details to come.
Apr 28-29 - Project presentation/demonstration. Each team
will meet with the instructor and TAs to show and explain their
visualization and describe
what they have done. You will need to submit through Canvas and
bring three copies to the meeting of a 1-page project overview
document. The document should include the following items: topic, team
member names, screen shot, problem description & project overview
Apr 30 - Project materials. Each team will submit all
their project files (Tableau projects, webpages, pdf's, etc.) to a
Canvas assignment. Include in this package a one-page overview
document of the project and the files being submitted.
May 3 - System video. Create a 3 minute or less video
that describes the problem you addressed and that showcases your
visualization. During our final exam period, we will have 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 May 3, 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.
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, do they 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. 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). 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
the poster session then you will be in deep trouble.
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.
Also, consider organizations that run contests for developing
visualizations. They can often serve as good sources of problems and
data. Some examples are:
Institute of Justice
vs. actual student interest, spatio-temporal analysis
And finally, there are websites that can help you find data sets such
public datasets, Kaggle
Dataset Search Tool.