Due Dates:
Kickoff Meeting: February 24th (Friday, Optional Class)
Data Set Selection: February 24th (All data sets must be approved)
Rough Drafts: March 7th (on Tuesday, and bring to class Wednesday)
Designs Turned In: March 14th (on Tuesday, and bring to class Wednesday)
Written Critiques: March 29th March 31st
Designs online! http://graphics.cs.wisc.edu/Courses/Visualization17/design-challenge-1/
Objectives: To make some visualizations with real data, and to explore how to tell different “stories” by choosing different encodings of the data. This is a chance to try out using visualization tools. If you’re an 838 student, you’ll get to try your hand at dealing with a larger data set.
Overview
In this assignment, you’ll pick one data set to make visualizations from. Then, you will make 4 visualizations – each telling a different “story” about the data. Then you will also make a 5th visualization that re-tells one of the stories from the first 4. The idea here is that you should explore the different kinds of visualizations you might make from this data, and the different questions/tasks that you might want to show someone, and to see how you can match the picture.
We will provide a bunch of choices of data sets. We will check to make sure they are sufficiently challenging (there are good stories in them), yet not too hard in ways unrelated to the class (e.g., they need extensive cleaning or specialized science to interpret them). We encourage you to pick one of our data sets.
For this year, we will allow people to “bring their own data set” subject to a bunch of rules. The data set must be publicly available, must be on a topic of general awareness (i.e., not something that only researchers in a specialized field care about), and must be sufficiently challenging to work with. In order to use a data set not on our “approved” list, you must get our approval. We will have a “bring your own data day” (Feb 24) where you can bring your data set for public critique (and possible approval). If your dataset is approved, it will be added to the “list of approved data sets” so that anyone in class can use it. No new data sets will be approved after Feb 24.
You may use any tools that you like to create the visualizations – subject to the constraint that you are required to hand in PDFs, and to document your process. It is fine to use Excel or Tableau or R or JMP or some other “tool.” It is also fine to write your own programs that create visualizations in whatever programming language you like. There may be practical issues in getting pictures our of your own programs – at worst, you can use screen capture. We will have class sessions where we show off some tools you might want to use, as well as using Friday class time for “help sessions.”
For rough drafts (due March 7th) sketches are fine – the goal is to get feedback from others. We encourage you to do a lot of sketching to try out different designs (even if we can’t give feedback on all of them).
For the final ones, you should make real visualizations with the real data.
If you find that you aren’t able to exactly implement your design (e.g. you can’t figure out how to convince excel to use the colors that you want), feel free to “cheat” a little (save the picture and open it in Photoshop and paint over it), but part of the idea is to try to make pictures with real data (so don’t just sketch – unless you are doing precise measurements). If you’re really stumped on implementation, you can put a note in your caption “the red dots were supposed to be blue” – but try not to leave too much to the imagination of the viewer.
On Tuesday, March 7th, you will upload at least 2 sketches (either as PDF or image files) to Canvas, and tell us about which data set you are using. You should bring printouts of 2 designs to class – we’ll take some time to critique each others work.
On Tuesday, March 14th, you will turn in your “final” visualizations (at least 5 – since for one of the stories you need to make 2 visualizations). For each visualization, there should be a good caption, explaining the data and enough of the story. Although, if your graph is really great, the reader might figure out the story without reading the caption. Please do not put your name inside the PDF (so that we can send them out for anonymous critique). The PDFs should be 1 page each. it should be clear from the visualization and/or caption what data set it is. Turn in a 6th document that explains how you made the pictures, and what you were trying to show with each one. These will be turned in as an assignment on Canvas.
Please bring a printout of at least one of your visualizations to class on Wednesday, March 15th. We’ll do some in class critiquing. (although, it is too late for you to change your visualizations).
Shortly after March 15th, we will send each person a few visualizations to critique. We will grade you on your critiques. We will also provide the critiques back to the designer (note: student critiques will not determine the grades for the designs – the class staff will grade them).
How to do this?
We are explicitly not specifying how you should make your visualizations. Given the range of skills of students in the class, there isn’t one tool for everyone.
Our main interest is in the results. Good results are visualizations that effectively tell the stories they are trying to tell. How those visualizations are made is less important than how well they work. Well-chosen, basic charts can often tell interesting stories, but we would like you to try to tell richer, more complex stories.
We do encourage you to use this assignment as an excuse to learn about new and different tools. We intentionally added some extra time at the beginning of the assignment for people to do this. That said, this isn’t a time to go overboard: if you’ve never programmed in JavaScript before, now might not be the time to master D3. But, it might be a chance to try out Tableau – even if you decide to make your final pictures some other way.
Part of this assignment will require you to do some quick looking over the data set to see what stories are there – this is “exploration” (in statistics, they might call it Exploratory Data Analysis). The tools you use for this kind of exploration might be different than those you choose for making your final pictures.
We’ll have some in-class sessions to help you get your handles on tools:
- Wednesday 2/22 – we’ll show off some ways you can use excel to quickly explore data and make some pretty nice charts.
- Friday 2/24 (optional class) – while the focus of the “kickoff” event is to discuss data, part of this will include looking at data, which will require us to practice using tools.
- Wednesday 3/1 – We’ll give a little bit of a getting started guide to Tableau.
- Friday 3/3 (optional class) – this “help session” will be a chance to ask questions about tools. If we can’t help you, maybe a classmate can.
Data Sets
You can choose any of the data sets on (link coming).
If there’s a data set you want to see on the list, submit it to us (and bring it to the optional class on February 24th). If we agree it’s good for the assignment, we will put it on the list for anyone to use (including you).
Examples
We have a similar assignment in 2015 (assignment posting). The data sets were a bit different, and the class was half undergrads (it was a 638). But you can see the results here. (you can’t follow the links to see anything other than the thumbnails – but it can give you a sense of what kinds of things students did in the past).
Data and Example Questions
Try not to pick questions that can be answered with a single statistic – but something where the visualization adds value. The richer and more complex the task the story (or sets of stories) that the visualization tells makes it more interesting (and challenging), and gives you more opportunities to make a particularly cool “story”.
For example with the airline data (a month of flight delay information):
- You could give the statistics on the average delay for flights leaving Madison
- You could give the statistics on flight delays leaving Madison, helping someone choose which destination has the least delays, or what time of day you are most/least likely to have a delay, or some combination of both.
- You could present information on a bunch of city pairs – for example, to help someone plan a trip between Madison and San Francisco, which hub city is it best to connect through? what time of day should you leave? (if your goal is to avoid delays)
We’ve picked the data sets (but you get to choose amongst them). You get to pick the stories to tell. Think about stories that someone would care about. Stories that would be interesting.
Grading / Turning Things In
Choosing a data set: we will not require you to tell us what data set you are choosing before the rough drafts are due. However, we recommend that you pick your data set early. If you want to have us add a data set to the list of data sets, follow the procedure above – but asking us to add a data set to the list will not impact your evaluation.
Rough drafts: due Tuesday, March 7th. Upload (at least) 2 PDF files (or other image files) to Canvas. And bring at least 1 on paper to class on March 8th. These will be graded check/no check (i.e., nothing/something/acceptable x late/not late – on our weird numeric scale). We may provide some feedback, but mainly we want to make sure you’re working on the assignment. We will take some class time to have people critique each other’s drafts.
In addition to uploading your PDFs of 2 designs, in the type-in comments, please tell us which data set you are using (although, it should be obvious from the vis), and what story you are trying to tell (optional if the vis is self-explanatory).
Designs Due: due Tuesday, March 14th. This is the “main hand in.” This will be turned in as an assignment on Canvas (LINK available in the future).
You need to upload 5 designs (4 questions, 2 designs for 1 question). You may submit 1 or 2 extras. Each design should be a separate PDF file, and be self-contained with a caption. However, it should not have your name on it (so we can send it out for anonymous critique).
As an additional document (either as a PDF or in the Canvas type-in box), explain how you made the pictures, and the questions that each is meant to address (hopefully it will be clear from the vis and caption). Your peer reviewers will not see this document, but the grader will.
We encourage (but not require) you to turn in a ZIP file with the “implementation” of your visualization. In particular, if you wrote software yourself we will be curious to look at it (just the source). It won’t count against you, but if you did something particularly clever, please show it off! (whether its clever scripting, or amazing use of excel or …)
The course staff (probably the TA and the instructor) will assign a grade (unclear if we will use a numeric scale or an A-F scale). The grade will be for the quality of what is turned in (other parts of the assignment, and penalties for being late will be added later).
The things we will consider include:
- How good/interesting are the “stories” that you chose? Did you pick a diverse set? Are the things you chose to show multi-variate?
- How well chosen are your encodings? Are they effective at communicating the message?
- How well “implemented” are the designs? Are the specific detail choices made thoughtfully?
Visual appeal and implementation (beyond what is required for effectiveness) may be rewarded, but are not central.
Note: if your assignment is too late, we won’t be able to send it out to get peer reviews. Also, we won’t give you things to peer review until you submit your own.
Peer Reviews: due Wednesday, March 29th Friday, March 31st. Shortly after the 14th, we will assign each student a few of their classmates designs to critique.
We will grade the critiques (so you need to write good critiques!) and give the feedback to the designer, but the critiques will not be used to determine the grade for the design. The exact mechanism for critiques will be provided later, but it will be graded just like discussions.
We have sent all students an e-mail with their assigned critiques. Please fill out the critique form for all of your assigned critiques for full-credit.