Design Exercise 2: Stories from Life Expectancy

in Design-Exercises

This design exercise has you create some visualizations from the Life Expectancy data set. You will turn it in on Canvas as Design Exercise 2: Life Expectancy (stories) (due Fri, Oct 23).

At A Glance

This assignment asks you to make 5 visualizations from the Life Expectancy data set. One visualization (Part A) is a provided question, a pair of visualizations (Part B) is an “exploration loop” where you refine a question, and the other two visualizations (Part C) should tell stories of your own choosing.

You will turn the assignment in on Canvas at: Design Exercise 2: Life Expectancy (stories).

On-time assignments (by 12:15pm, Friday, October 23) will have the opportunity to participate in a feedback activity to earn extra points, and to resubmit by the late cutoff (11:59pm, Wednesday, October 28).

Assignments will be rewarded for the effectiveness and quality of the visualizations and the quality of the questions/stories that these visualizations tell.

This assignment asks you to create visualizations that effectively “tell good stories” (i.e., help the viewer with some understanding task) from the Life Expectancy Data that you have been working with. It is described at Life Expectancy data set.

The idea of “what is interesting” is definitely subjective. And it is weirdly skewed: part of “interesting” is “leads to an interesting visualization.” So, part of this assignment is not just to be able to recognize interesting things in a data set, but to recognize what kinds of “stories” can be told visually.

Our goal (for this assignment) is to make visualizations, not necessarily to do good social science. You should document what you observe - not necessarily try to draw causal conclusions (which probably requires data science beyond the scope of this class). What are we looking for? should provide a sense of what we are looking for.

You will turn the assignment in on Canvas at: Design Exercise 2: Life Expectancy (stories). The form has 19 questions: 5 upload your visualizations, and the rest are text responses (plus one yes/no). Questions 16-19 are only for re-submissions (see The Feedback Round). Each question is listed below, in the description of the part it belongs to. You should prepare your answers offline (and images).

On-time assignments (by 12:15pm, Friday, October 23) will have the opportunity to participate in feedback activities that can earn extra points, and to re-submit their assignments by the late cutoff (11:59pm, Wednesday, October 28). Details are provided below.

For each visualization you are required to provide a rationale. The rationale is an opportunity for you to explain the design choices that you made to tell the story well.

Some Ground Rules…

In no particular order…

  • You need to create static visualizations: things that work in “print” (or just as an image file). If you create something interactive, we won’t see the interactivity.
  • Each visualization must be a single “picture” (one page, preferably PNG - if it’s a PDF it can only be 1 page). You can put multiple charts together into a single image, but remember - it must fit on a page/screen.
  • You may use whatever tools you like to produce the visualizations. The same rules from Design Warmup 2: Make Some Visualizations apply (repeated in the expand box below). Notably, be careful in your use of AI.
  • We aren’t doing the actual social science here - these graphs are descriptive. We are showing (at most) correlation, not causality.
  • We prefer that you do not use the questions that we (the course staff) have already posed in previous assignments.
  • You should not add data. You can use knowledge that you have (for specific things, like events). But you shouldn’t try to augment the data by joining it with some outside source (like finding the GDP for each country). One exception: you can use geographic information (for example, to make a geographic map).
  • We are not asking you to provide your intermediate files (e.g., the programs you write or your Tableau workbooks). We do ask you to explain your process. We may ask you to show your work (e.g., provide programs) later, so please keep the intermediate files.
AI Policy Details for Design Warmups and Design Exercises

I am giving you (students) broad permission to use AI tools/agents, and relying on your judgment to use it in a responsible way that does not get in the way of your learning. Rather than trying to specify what is or isn’t acceptable, I ask that students use their judgment, honestly disclose their usage, and reflect on it.

The broad range of AI use makes a simple policy hard. When you create visualizations, AI tools can be anything from an assistant on using visualization tools to an automated approach to creating visualizations. You might try everything from “make the visualization specified in this assignment” to “tell me which button to push in Tableau to change the marks from red to purple.”

The AI tools can also do other aspects of data analysis. You can ask an AI agent “make 5 different visualizations from this data set showing different aspects of it, and explain what each one shows.” Or even “find something unusual in the data.”

For me, there is no clear line between “AI does it for you” and “AI is helping you do it by taking care of the boring bits.” So, I ask you to use your judgment: use AI tools in a manner that fits the spirit of the assignment, where you are getting the experience of designing the visualizations and practice at making the decisions and thinking about how to effectively show the things that you want to show.

Having AI do too much for you gets in the way of you experiencing the designing and creating, which I think is essential for learning.

Aside: Learning about AI tools

The goal of this class is for you to learn about visualization. You might use AI tools to help you make visualizations, to design them, or even to learn about them.

There is a different goal: learning about AI tools. Rather than learning about visualization, the goal could be to learn how to use AI tools to efficiently do work (for example, assignments asked of students). This is not the goal of this class.

Asking your AI agent to write programs to process data, or even to help write programs to make visualizations you describe, is generally acceptable.

My goal is that you should learn about visualization. You can actually learn a lot by using an AI agent and, when it does something for you, asking why.

You should be involved in the choices of how visualizations are created. Generally, this means making the choices of which environments/tools are used (e.g., to use Python and a particular library). However, you might ask an AI “We are going to make some (describe visualizations) by writing Python programs. What visualization library might you recommend?” (I use Python as an example: AI agents seem to know about many languages and libraries. They are also good at reading the tutorials for interactive systems like Tableau.)

So, I am going to trust students to use AI responsibly. This means following the main rules AI Policies. But some specifics:

  1. I will explicitly ask if and how you used AI tools for the assignment. Please be specific about what tools you used and how you used them.
  2. I would like your prompts (there will be a separate question for that). Please provide them as much as possible. This is mainly for me to learn from.
  3. I will ask “What did you do to make sure that the AI tools did not get in the way of your learning.”

If the goal is to learn about visualization, trying to one-shot prompt to get an AI agent to do the assignments is unlikely to give you the experiences that help you learn about visualization design. If the goal is to see if current AI models are capable of doing these assignments, trying the prompts yourself is a waste of your tokens: the course staff has tried it, and has confirmed that the AI tools are good at it. You won’t learn much by doing it.

Ask questions to the course staff if anything is uncertain.

Part A: The Life Expectancy Data - Staff Provided Question

Here’s a very broad question: Women generally live longer than men. Is this changing over time and/or place?

Create a visualization that tells a story around this question. Try to expose some interesting fact or pattern. For this, you must use the world bank version of the data (as it is the one that is split by sex).

Question 1: provide your visualization

Question 2: explain what you see in your visualization, and give the rationale for the design choices you made that help you see this.

A hint… trying to show all 150 countries over 60 years (approximately) might be too much to fit into a single visualization. Consider how to reduce things using selection or aggregation.

Part B: The Exploration Loop with the Life Expectancy Data

You can think of exploration as the “task of trying to identify interesting questions”.

Your goal is to find interesting questions/stories and create visualizations that do a good job answering them. Of course, the data has to cooperate - you might have a good question, and look at the data the right way, but the answer might not be so interesting.

Since you don’t know the data, you don’t know what stories it holds. You need to do some exploring to find the stories. Or, you might be lucky and find something quickly.

The process is: ask a question; make a visualization that should answer the question; and then (iteratively) either refine the question, refine the visualization, or move on to a completely different question (either because you succeeded, or because you are giving up).

What you hand in is two snapshots from this process: an early one and a later, more refined one. You may make other visualizations in between that you don’t show us.

Question 3: Your initial question. (It shouldn’t be a question from another part of the assignment.)

Question 4: Your initial visualization, one that attempts to “answer” the question from Question 3. A rough visualization is OK. “Good enough to help you decide what to do next” (see Question 5) is the goal.

Question 5: What you see in that visualization, and how your design choices expose it. Then say where it led you: your refined or new question (or how you are now looking at the original question differently).

Question 6: A later visualization that addresses the question from Question 5. It doesn’t have to be the next one you made; there may be steps in between, but the connection should be clear enough (from Question 5). This should be a refined visualization.

Question 7: What you see in this visualization, the question or story it is meant to tell, and the rationale for your design.

Part C: Two Stories - Your Choice

Create two more visualizations from the Life Expectancy data. Each should “tell a story” (of your choice).

Your two stories should be about different things: not the same finding shown two ways. We also value variety in design. Two line charts with different variables shows less range than two different approaches.

For each visualization, you will be asked to provide your rationale for why you think it is effective.

A Part C story may grow out of your Part B exploration, but it must be a different question and a different visualization from your Question 6. A more polished version of your Question 6 visualization doesn’t count.

Question 8: Provide your first visualization

Question 9: Explain what you see in this visualization - and how and why your design exposes things. Provide a rationale for your design.

Question 10: Provide your second visualization

Question 11: Explain what you see in this visualization - and how and why your design exposes things. Provide a rationale for your design.

Summary Questions

Question 12: Explain how you made these visualizations. Give details about your tool choices and any data processing that you performed.

Question 13: How did you use AI tools in completing the assignment? Please be specific. If you didn’t use any, say so explicitly.

Question 14: Provide the prompts that you used. If it’s too hard to put it into the type-in box, say that you have a ZIP file (and we will make arrangements with you to deliver it to us). (say N/A if you didn’t use AI tools)

Question 15: What did you do to prevent AI from getting in the way of your learning? What did you do to have AI enhance your learning? (“I didn’t use AI” is an answer to this).

What are we looking for?

A hint from a past version of the class:

… we are looking for explicit choices you made to emphasize your “story.” A “data dump” (just making a chart of some of the variables) is not likely to get you a good score. If you make explicit decisions - selecting subsets of data, highlighting particular points, arranging designs that emphasize certain aspects, etc. - this will be rewarded.

Some notes on “A Good Question”

From an old version of the class:

For the purposes of this exercise, a “Good Question” has a non-standard (but still hand-wavy) definition: it will make for a good visualization.

I should also say that I am using the terms “Good Question” and “Stories in the Data” in a very similar way. The idea is to find something interesting in the data: you might phrase it as a question the data answers or a fact/pattern in the data (that would answer a question).

A good question/answer should be:

  1. Interesting (non-obvious, ideally it is something that the course staff who has looked at this data hasn’t already seen; uniqueness is valued)
  2. Data rich
  3. Taking advantage of visualization (Generally, this means the answer is complex. It could be a simple yes or no, but the visualization would have value to give context, or support the answer, or help address followup questions, …)
  4. Can actually be answered from the data (you could actually make a good visualization to answer the question)

While it is somewhat circular… a good question/story is one that leads to a good visualization to answer/tell it. Yes this is kind of weird: you can (should?) pick stories that let you show off your ability to tell stories with visualization.

For this assignment, what we are looking for in a “good visualization” are things such as:

  • Is the question/story interesting and clear?
  • Is the design effective? (is it well adapted to the story/task?)
  • Do the details represent good choices?
  • Is the design appropriate for the data?
  • Is the rationale properly stated (in the documentation)?
  • Is the design complete (it has enough of a title and caption that it stands alone)?

Of course, you should avoid “bad” practices (e.g., inappropriate encodings or aggregations).

The Feedback Round

If you turn in a complete assignment on time (by 12:15pm Friday)…

This deadline is strict: on Friday afternoon the course staff will assign you visualizations to critique. (exact mechanism to be specified). You will earn 1 point for each visualization that you provide “useful” critique for (you can only earn for up to 2 critiques). To be considered useful, the critique must:

  1. be a good critique (i.e. the course staff thinks will meaningfully help the designer).
  2. be timely (provided before end of day Sunday, Oct 25), so the designer has time to consider it,
  3. must not be redundant with other feedback provided. You can either be the first to provide feedback on a design, or you can provide different feedback that is useful beyond what others have already given.

The feedback mechanism is likely to be a Canvas discussion so that you can see feedback from others.

Course staff will also contribute critiques to the discussions.

The idea is that you should be able to learn from doing the critiques, from critiques of your work, but also seeing the critiques to others.

If you turn things in on time, you may re-submit your assignment. You will need to re-upload all of your answers (sorry, this is a Canvas thing) (Update 10/26: you no longer need to re-upload everything. See Resubmitting below.) When you resubmit, you also answer Questions 16-19 below. You will get 1-2 points for answering these well. Note: even if you decide your assignment is sufficient, you can say “I didn’t change anything” (and give a few sentences on what you learned from reading and writing the critiques) and still earn 1 point (providing you really didn’t change anything). The second point is for actually improving your assignment.

Resubmitting

Update 10/26: This replaces our earlier instruction to re-upload your whole assignment.

If you turned in on time, you can resubmit as many times as you like before the cutoff. You don’t need to redo the whole assignment. We grade the most recent answer you gave to each question. In a resubmission, answer only the questions you changed, and leave the others blank. (Writing “no change” also works, but blank is safer.) For the questions you leave blank, we use your most recent earlier answer.

  • A new answer replaces your old one entirely. If you revise a text answer, give the whole revised answer, not just the changes.
  • To change an image, upload the new version. If you don’t upload one, we keep the one you submitted before.
  • Leaving a question blank keeps your old answer. You can’t remove an answer by leaving it blank.
  • Questions 16-19 work the same way. If you resubmit more than once, your most recent answers to 17-19 replace the earlier ones. So they should cover all of your changes, not just the latest round.
  • If you change a visualization, check that its rationale still describes it. If not, resubmit the rationale too.

Re-uploading everything (as we originally asked) still works.

Re-submission Questions

Leave Questions 16-19 blank (or answer “No” to Question 16) if this is not a re-submission of an on-time assignment.

Question 16: Is this a re-submission of an assignment that was previously submitted on time?

Question 17: What did you learn from the critiques: the ones you received, the ones you wrote, the class discussion, and the ones you read? You might also have learned from indirect feedback (for example, in-class discussions of examples) - be clear what you are referring to.

Question 18: What did you change in your designs and rationales? Be specific about which visualizations and/or text answers changed. If you didn’t change anything, say so explicitly (“I did not change anything”). If you re-submit more than once, list all of your changes, not just the latest ones: this answer replaces your earlier one.

Question 19: Why did you make those changes, and why is your new submission better? If you didn’t change anything, explain why you think your original submission was sufficient.

So, if you turn in a complete assignment, you will have the opportunity to earn up to 4 extra points (up to 2 for critiques, up to 2 for the re-submission questions).

More details will be provided after the deadline to students who have turned things in on time.

Rubrics

There will be a separate rubric document with a more detailed rubric.

The grading criteria will be discussed in class. We will try “grading” some example assignments together in class as critique practice, but also to communicate grading expectations. We expect this process to refine the rubric, so we will not publish a detailed rubric until after that class.

We may refine the rubric after the deadline (but before the cutoff) which may help students improve their assignments in the feedback round.

The visualizations (Parts A-C) and their associated questions will be graded on the standard 90=A numeric scale. There are 4 main parts (Part C is really two visualizations). We will weight Part A a little less (since we give the question), and Part B a little more (since it involves 2 visualizations and more questions) - roughly 20%/30%/25%/25%.

For the Summary Questions: we will deduct up to 4 points for insufficient answers.

The rubric will provide an updated version of the 2025 Evaluating Designs page. You can look at last year’s page if you want a preview.

We will identify elements in your answers and assign them with “Levels of Correctness” (Bad, Ugly, Good, Splendid, as described in class or on the rubric/evaluation pages). We will consider the types of design (again, from class and pages). These definitions give a strong hint for what we will be looking for in assignments.

We will try to make the numbers work out so:

  • 91 or higher (high A, A+) - Many splendid aspects and no bad ones. Good outweighs ugly.
  • 90 (low A) - significantly more good than ugly, no bad, some splendid
  • 85-89 (AB) - some good elements (but balanced by ugly), few or no bad
  • 84 or lower - generally some bad elements, and a bad balance of ugly to good

Basically, we will look at your visualizations and assign codes. We will consider the different aspects (also on that page). There is no simple numerical connection - there are too many different possible solutions (see types of designs on that page). We want to reward well done standard designs as well as ambitious novel ones.

Grading will consider the quality of questions.

GenAI Disclosure:

I used Claude Code (Opus 5.5 inside of VS Studio Code) to provide feedback on my draft (I asked it what wasn’t clear). It made suggestions for re-organization that I implemented myself, and made some of the minor edits itself.