Dear data - set up
Begin with inspiration.
In my classes, I talk about visualization as art, as well as art as visualization. (What ‘counts’?)
Dear data - lab
Curate a small dataset. I recommend fewer than 10 rows, but at least two variables.
|Bde Maka Ska
|Lake of the Isles
Bring craft supplies!
Dear data - final submission
Your first visualization assignment is to create a hand-drawn visualization about data from your own life. This assignment is inspired by Giorgia Lupi and Stefanie Posavec’s book, Dear Data, so you may want to look to their work for ideas.
In the selection of the text I’ve posted, they suggest:
- see the world as a data collector
- begin with a question
- gather the data
- spend time with data
- organize and categorize
- find the main story
- visual inspiration to build your personal vocabulary
- sketch and experiment with first ideas
- draw the final picture
- draw the legend
I don’t care what you pick as your question, as long as it interests you. Some ideas from Dear Data include:
- thank you
- phone addiction
- to-do lists
- positive thoughts
- urban wildlife
- negative thoughts
I would like you to collect data for a week, so you should start thinking about your question over the weekend, and perhaps start collecting data next week. I’d prefer you to visualize something that is not automatically tracked by your phone, but I won’t be too picky.
For this assignment, I am requiring two deliverables:
- a finished visualization, complete with handwritten legend
- an accompanying description of the visualization, including
- the question you decided to answer
- the first few ideas you came up with when brainstorming
- why you chose your final visualization method
- a description of the variables you visualized, and the visual mappings (e.g., “I chose to map the number of steps I took in a day to color, with red meaning very few steps (less than 1,000), orange meaning a middling number (1,001-5,000), yellow meaning I did alright but didn’t hit my goal (5,001-9,999) and green meaning I hit my goal or surpassed it (10,000+).”)
These deliverables can be handed in to me physically in class or scanned and uploaded to Canvas.
Dear data - assessment
Already we have the problem of assessment. I tell students that I won’t be grading them on their artistic skills, but I want a high-quality product. How can I define that?
My rubric is pretty bare-bones.
Data visualization. There is a data visualization. 
Visualization is hand-drawn. Visualization was made by hand, ideally on paper (or other physical media) but okay if done by hand on an electronic tablet. If done electronically, marks should all be done by hand, without things like automatically-perfect circles. 
Legend. Legend exists and explains all encodings. Legend is also hand-drawn. 
Description document exists. 
Question. Description includes question answered. 
Other visualization ideas. Description includes other visualizations brainstormed before the final method was chosen. 
Mappings. Description includes mappings between variables and marks. 
A week’s worth of data. It appears that the visualization includes data collected over the period of a week (or more). 
One number story
“Keep the number of digits in a paragraph below eight.”
“You’d be over your allocation with a sentence like this: The Office of Redundancy’s budget rose 48 percent in 2013, from $700.3 million to $1.03 billion.
Think about how it could change:
Over the past year, the Office of Redundancy’s budget grew by nearly half, to $1 billion.”
– Sarah Cohen, Numbers in the Newsroom
One number story
Focus on one number (but use more numbers to contextualize it!)
That number might be the mean, the median, the maximum, the total…
Use simple data tools— in my class, we use spreadsheets for this assignment (sort, summarize, pivot tables).
- “Boston Wins The High School Dropout Race”
- “Massachusetts Academy of Math and Science Remains Atop the Podium”
- “10 High Schools in Massachusetts had a Perfect Graduation Rate in 2016”
- “New Century School Math Achievement Grows Again”
- “Math achievement lower for SLP students of color”
One number story
This is an assignment with several iterations
- first draft
- peer editing
- final draft
One number story - peer editing
You will end up reading your peer’s piece multiple times, in order to check all of the important components. Here are some guiding questions to consider as you work, although you don’t need to respond to all of them, and there may be more things that strike you that deserve commentary.
Introduction: Read just the headline, the lede and the nutgraf. Do they hook you? Do you want to keep reading? Why or why not?
Flow: is each sentence clearly connected to the one previous and the one following? Do you have a sense throughout the beginning of the article that things are in the “right” order? Context: Do the introductory paragraphs provide a sense that there’s a reason for this piece? Do they give a sense of the larger problem that will be addressed?
Nutgraf: What is the nutgraf? Locate it in the rough draft: is it clearly stated? Help the author decide if it is two broad, too narrow, or just right. Work on rewording if necessary.
Sentences: Read the rest of the piece, paying close attention to each sentence and to the flow of one sentence into the next. Are there mistakes in grammar, usage, spelling, or typing? Mark them on the draft. Do the sentences flow nicely, or do some of them feel as if they need reworking? Choose two sentences that you feel may need work, mark them on the rough draft, and make suggestions for possible revisions.
Paragraphs: Look over the paragraphs. Does each one feel like a unit in and of itself, with an introductory sentence, body sentence(s), and a transition sentence moving you into the next paragraph? Are any of the paragraphs too long? Evidence: Is there adequate evidence in the piece to support the author’s argument? Are there too many numerals, or too many quotes, overloading the author’s voice? Does the author leave out any quote or bit of evidence that seems particularly obvious or helpful to you? Are any quotations cited?
Analysis: Does the author explain how each number or bit of evidence supports their point? Link: Is there a statement at the end of each sub-argument explaining its relationship to the larger point? Are you ever confused about why a bit of the piece exists or how it’s related to the author’s argument?
Conclusion: Is it satisfying? Does it tie up loose ends? Does it provide a larger context for thinking about the paper’s subject? Does it answer the ‘so what’ question? Data analysis: Glance through the data operations. Do the numbers and conclusions drawn by the author appear appropriate? Can you spot any obvious mistakes? In particular, pay attention to the “compared to what” problem– is the author comparing apples to apples?
I would like to see you give some substantive thought to these peer reviews, so I’d expect to see around 10 sentences in the comments. As always, use the sandwich model– start with something you liked, or was particularly strong about the piece. Then, comments on how the draft could be improved (remember, we’re going to do a final draft of this piece!). Conclude with another sentence or two about things that worked with the piece.
When I grade the final pieces, I will be looking for the following elements and considerations, so keep these in mind as you edit:
- Context of data
- Spelling and grammar
- Clarity of writing
One number story - assessment
Again, I’ve developed a rubric but it’s pretty bare-bones.
- Headline 
- Byline 
- Lede and nutgraf.  great lede and nutgraf  so-so lede and nutgraf.  no lede/nutgraf
- Exposition.  good exposition  exposition unfocused, unorganized  no exposition
- Appropriate length.  good length  too long/short
- Data context.  great contextualization  no contextualization
- Spelling and grammar.  no obvious mistakes  occasional small errors  many distracting mistakes
Lightning talk - set up
I provide a number of examples of “data-adjacent” lightning talks. For example,
We watch several talks as a group, and discuss strengths and weaknesses of the talks.
A 5-minute talk on something that is “data-adjacent.”
- Describe a particular R package
- Talk through an interesting data analysis someone else has done. You might look through your Data Dialogues for ideas, or page through sites with data journalism like The Upshot, FiveThirtyEight and ProPublica.
- Find a connection between a hobby and data science. I once saw a lightning talk at NICAR that made a connection between Pokemon and data, there have been talks about woodworking and data, cats and data, etc
You will video-record your talk, and upload it to the internet so that your peers and I can watch it. As you look through the example lightning talks I have linked below, you will see that the people who did the talks were designing their talks to be delivered live, in front of an audience. This means that the video of the talk is perhaps not as professional as it could have been if the presenters were creating the talk to be seen as a video. In order of video professionalism, I would rank the NICAR talks the least professional, then rstudio::conf, then eyeo (most professional, almost as if the talk was designed for video). I would like you to aim for more professional, because you are designing your talk to be viewed as a video.
Lightning talk - peer editing
I have students submit their first draft of their lightning talk as a discussion post on Canvas, so any other student can watch their talk. Students are assigned three peers to peer review, and they provide their feedback as a comment on the discussion thread.
I’d like you to put your comments in the same discussion thread as the talk you’re commenting on, and aim for a compliment sandwich:
- Start with one thing the talk did well, you found interesting, you liked, etc.
- Then any room for improvement you can see. I would like you to find at least one thing to be critical about. Does the person need to work on saying “um” less (I know I do!). Are their slides too busy and hard to read? Etc. I know it’s hard to give critical feedback, but that’s how we improve!
- Finish off with one more positive thing.
Lighting talk - assessment
This is a place where I am especially poor at providing feedback. I don’t even have a rubric!
I am strict about timing, and tell the students they have a “time limit of 5 minutes long, give or take a minute (in other words, it can be as short as 4 minutes or as long as 6 minutes).”
When they post their final submission, I ask them to summarize some of the changes they made between the first and final draft.
Critique is one of the most valuable components of a formal art and design education. It is also one of the most difficult aspects of the art and design school experience, especially for new students. Critique is a collaborative activity that takes quite a bit of time to learn — both in terms of how to give feedback, and how to accept feedback. While there are no hard-and-fast rules to the critique process, this site is intended as a helpful guide for those just starting out.
From How to crit by Mitch Goldstein.
Giving a crit
The most important thing about giving someone a crit is that you should always be kind instead of nice. A nice crit is telling someone their work is pretty good just to avoid hurting their feelings. A kind crit is telling someone their work is not where it needs to be so they know it needs to be improved or refined. Be kind and honest, instead of nice and disingenuous. Also make sure that your feedback is not derogatory, insulting, or dismissive of the person in front of you. Remember that giving a good crit has absolutely nothing to do with being mean.
The Trouble With Correcting
Generally you should try to avoid giving corrective critiques — comments like “I would do it like this” or “you should try it like that.”
From How to crit by Mitch Goldstein.
The final project is worth 20% of overall course grade. Again, there are many stages of the assignment and I find it difficult to assess the early stages. Again, I will mark down in the final draft if the student left work incomplete after feedback.
Discussion posts, readiness quizzes, and participation
Assessing weekly work
25% of students grade is related to being prepared for class and participating. I assign weekly readings, which I try to ensure students have done by assigning readiness quizzes (auto-graded multiple choice or similar) and “data dialogues.” I suspect students aren’t really doing the reading.
The recurring theme for our discussion posts will be a “Data Dialogue.” A data dialogue combines an element from the readings of the week with a data visualization or other data product you have found in the wild.
Entries can be short, just a few sentences, but should strive to explain briefly what the data product is, how to read it, and how it connects to the reading(s) of the week. Please provide a link if it is relevant.
You are broken into two groups:
Group 1: last names starting with A-J
Group 2: last names starting with K-Z
Each week, one group will write data dialogue entries, and the other group will respond to their colleagues’ posts. Responses should strive to find another connection between the data product and the reading(s).
For this week, Group 2 will start, and Group 1 will respond.
Data dialogues - example
Here is an example data dialogue post and response. I have lightly edited this from real student work I received in a prior semester:
Original post: “I found another life expectancy chloropleth map, this time focusing on Europe. In the reading, Muth refers to a Datawrapper feature that confirms whether your visualization adheres to appropriate levels of accessibility to the colorblind, a useful tool for ensuring inclusivity. I think the color scheme in this chloropleth works well, although I’m not sure if the scheme is accessible to those with color-blindness. I find the placement of additional information (Highest/lowest regions) to be clever in utilizing space with no information recorded. I’m curious how areas are sectioned off as most countries in the visualization are split into smaller regions.”
Data dialogues - example
Response: “I think this is a really visually pleasing visualization, especially as you note, the additional information and contextualization are in really good places. I think this is an interesting way to look at life expectancy because oftentimes the same data might be compiled and used as one color for the entire country. I found it especially interesting when Muth talked about considering using the smallest units possible. In this map, they have separate regions color coded individually, and it makes it more descriptive too. It also makes me speculate as to the causes of particular regions having lower or higher life expectancy rates. It looks like they never say that the medium gray, I assume, is countries lacking in data compared to the light gray of Africa and the Middle East, which I assumed to be countries they were ignoring for this visualization.”
Data dialogues - assessment
Again, how to assess these? I typically use a 3 point scale, where 3 means good, 2 means they were pretty light on details, and 0 means they didn’t submit the assignment.
At the end of the semester, some students have learned a ton, and that’s very gratifying! But when I compute final grades, I often discover a student is going to receive an A or B without seeming to have learned much.
- Next semester, I’m going to try Perusall to attempt to have students engage more with the reading. We’ll see how that goes.
- Standards-based grading?
What are the standards? Feels like I’m right back in my (poor) rubrics. I’m not very good at avoiding “giving corrective critiques.”