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Critical Data & Visualization 🐣 Spring 2020

Welcome! This page will be filled with material and links throughout the semester. I recommend to bookmark it now

Quick Links

Course Description

Data collection and algorithmic processing are not only central to recent technical breakthroughs such as in Artificial Intelligence and automation but have created new economic paradigms where data equals value and shape political approaches to power and control.

Decisions based on algorithms affect society at large whether it’s changing the way we transport and distribute goods, influencing the things we buy or the news we read. The world that algorithms see is data. For the average person, however, data is seldom more than an abstract idea.

So, what exactly is data? How is value extracted from it? And why should we care? How can we ethically balance the positive uses of data-driven systems with the threats they pose to discriminate and infringe basic human rights? This course seeks to untangle some of these issues practically and theoretically.

Content

Week 1

Mapping the Landscape

Monday Class

Introductions.

Group Activity: Mapping the subjects of this course.

Take-aways:

  • Data is never “raw”, but always cooked.
  • “data”
    • from latin (‘given’)
      • how about "capta" (== ‘taken’)?*
    • used in singular and plural
  • data has no truth
  • Data and Data Infrastructures
    • looking beyond data as a resource
    • data is performative

\*J Drucker

Assignments:

Due this Wednesday (2020/02/19):

  • Do this assignment first (strongly recommended)
  • We will spend 50% of our time in this course coding. Having a shared foundation for this is extremely important. I will always be there to support and assist you with problems you encounter. For now, please work your way through Coding Foundation: Setup and Exercises and submit your work in the end.
  • Here is a thorough, interactive basic-javascript tutorial if you want to brush up your skills: Basic JavaScript. And here is Codecadey's version.

Due Monday (2020/02/24):

  • Read Critical Questions for Big Data by danah boyd and Kate Crawford. The linked version has some intentional notes that will help you. Please read the whole text despite below prompts being pointed at specific sections. There is no right or wrong, what counts more than anything is your own opinion. For each prompt, write no more than a short paragraph:

    • Introduction and Chapter 2
      • Why does Bowker say "'Raw data is both and oxymoron and a bad idea'" ? (pp. 663)
    • Section 1 (pp. 665)
      • What could be meant by the quote "'accounting tools [...] do not simply aid the measurement of economic activity, they shape the reality they measure'"?
    • Section 3 and 4 (pp. 668)
      • In which way is Twitter data limited?
    • Section 5 (pp. 671)
      • If you don't need to login to obtain certain data, then it is public and free to use. Or isn't it? Please share your opinion.
    • Section 6
      • No prompts here, but a very well written chapter that is relevant to everything we will be talking about this semester. Please enjoy.

  • How to submit the homework:

Optional/Related readings and resources:

*online version accessible through NYU library

Wednesday Lab

Find the Lab in detail here

Content:

  • how a browser meets a website
  • how a browser sees html
  • css and js, endless metaphors
  • review homework
  • review JavaScript data structures
  • collect data using Google Forms
  • Mini data visualization using javascript

Assignments:

Due Wednesday (2020/02/26):

  • Mini Project:
    • Create a Google Form collecting data of the "linear scale" type (like we did in this week's Lab)
    • collect responses from at least 10 people (e.g. send it to people in this class (use Slack or even Discord))
    • use the techniques used in the lab to
    • export the data in json format
    • transform it to an array with average values
    • build a bar graph using JavaScript (lab's code)
      • be creative and make it look more fun than my example!
    • the last two points can be worked on simultaneously (you don't need all the responses to start working on the code)
    • relvant links:
  • Watch this fun talk by Mike Bostock, creator of D3js.

Week 2

data types

Data (types or categories)

Monday Class

today's slides

  • discussion of "Critical Questions for Big Data"
  • data (types) exercise
  • art works I'd like to share
  • Announcing Data Zine Project

Assignments:

Due Monday (2020/03/02):

  • Read and add three contributions to this weeks reading assignment of "You Are Your Data" by Deborah Lupton. The text and the description of the assignment are in this document.
  • Listen (and enjoy) to this podcast: Artificial Intelligence: The Problem with Bias, with Kate Crawford. Here is a Spotify Link - if you have trouble accessing, please contact me.
  • Read through the Data Zine Project brief, especially the “The Data” paragraph, multiple times.
    • Define on a phenomenon that you will document/collect data about.
    • Name the features that you will take note of.
      Be poetic.
    • Check back with me on Slack if you are unsure or need help deciding between different options.
    • Start collecting your data tonight and present a week’s worth of data next week.

Wednesday Lab

Find the Lab in detail here

Content:

  • What is a library?
  • Hi, D3!
    • pixels vs. SVG
    • examples
    • Data Driven Documents
  • What you see when you see D3
    • Something dot something dot something semicolon
  • Code
    • Download working files
    • Part 1: The Start (Video)
      • selections, methods, attributes, return values, shapes
    • Part 2: The Whole Point (Video)
      • binding data to elements, "select nothing?!", enter-selection
    • Part 3: Jaws Drop (Video)
      • data functions
    • Part 4: Real Data (Video)
      • loading data in D3

Assignments:

Due Wednesday (2020/03/04):

  • Read the notes from the lab carefully and watch the videos.
  • Read them again, and email me questions you have. Book my office hours, too.
  • Use D3 to turn the dataset you are currently collecting (started after Week 2 class) into shapes.
  • Do not worry about visualizing the data effectively yet.
  • create any shapes from it and use data functions in at least one spot in way that the value of your data point affects the shape you created using D3.
  • push your work to your repo and submit a link to the class wiki by Wednesday (2019/09/18)

Week 3

human bias 1

Human / Bias I

Monday Class

Assignments:

Due THIS Wednesday (2020/03/04):

  • Upload a JSON file of the data you have so far self-collected to your repository and post a link to the class wiki.

Due Monday (2020/03/09):

  • "Automating Inequality" (2018) is a fantastic book by Virginia Eubanks that addresses specifically the who -- who is impacted by the process of datafication of society we discuss in this class. The book discusses the who that is not individuals, but groups of people.
    • This week, you don't have to read (you may if you wish; I can make the book available to you), but listen to an interview with the author Virginia Eubanks.
      • "Writer's Voice, Automating Inequality w Virginia Eubanks" (1 hour)
      • web link, Apple Podcast, it's on Spotify, too, but I cannot link to it for a strange reason, search for Virginia Eubanks, AUTOMATING INEQUALITY and find this image:
      • automating inqequality
    • Formulate short responses to the following prompts, no more than 400-500 words in total.
      • How to technical tools promise to "fair out" the remaining discrimination that exist in social/welfare systems? In how far can they succeed, in which ways do they fail?
      • Imagine, what could this (following quotes) mean in the widest sense?

        "The state doesn't need a cop to kill a person" and "electronic incarceration"

      • What do you understand this to mean?

        "systems act as a kind of 'empathy-overwrite'"

      • China is much more advanced and expansive when it comes to applying technical solutions to societal processes or instant challenges (recent example). Try to point example cases in China that are in accordance or in opposition to the problematics discussed in the podcast. Perhaps you can think of

        "technical systems not well thought-through about what their impact on human beings is"

    • Post your writing to the class wiki.
  • Watch Machine Learning and Human Bias (3 minutes)
  • Watch How I'm fighting bias in algorithms by Joy Buolamwini (9 minutes)

Wednesday Lab

Find the Lab in detail here

Content:

  • useful resources
  • Binding Data to Elements: Various Scenarios
    • a no-code introduction to enter-, update-, and exit-selections (slides)
  • incoming data: the enter-selection in detail (slides)
  • loading data and data functions: 2 "rules" to avoid errors
  • Classes: why & how we should use them (video*)
  • Groups: Structure (video*)

*videos can be downloaded on the same link

Assignments:

Due Wednesday (2020/03/11):

  • Visualize your data making use of group (<g>) elements
  • Your data points have multiple categories (names, values, labels etc.). Make each category affect a different aspect of a visual representation in a group.
  • Take this example by Giorgia Lupi. Each shape is a group of other shapes with attributes that represent different aspects of the data. She describes her logic on the back of the postcard. Create your own such logic for your data, and group shapes with different attributes into svg group elements. giorgia

Week 4

human bias 2

Human / Bias II

Monday Class

      MONDAY, 2020/03/23

*with thanks to Mimi Onuoha And Mother Cyborg (Diana Nucera); the activity is taken from their publication "A people's guide to AI" (2018)

Assignments:

Due THIS Wednesday (2020/03/11):

  • Read the following Chapters of The Visual Display of Quantitative Information by Edward R. Tufte:
  • Paper prototype for your Data Zine Project:
    • Print out the template on A3-sized paper, or this template on A4-size paper.
    • Sketch out where you are planning to put which information (graphic and descriptive text). Remember you can create more than one visualization to illustrate different aspects of your data set.
    • Scan your prototype, push it to your repository and add a link to the class wiki.

Due Monday (2020/03/16):

  • To take in:
  • To put out:
    • Think of contemporary systems/applications in which data is used to predict the future in order to then act upon it. Compile a list of 3 such situations that come to your mind and describe them briefly.
    • Collect your thoughts on the Prediction and its role in the three above sources. Your associations, opinions and ideas may be complemented by reflections on "collective average vs individual fate", "statistic vs. algorithmic prediction" or "social physics" (but don't feel obliged to discuss these). Express yourself in 300-400 words.
    • add your writing to the class wiki.

Wednesday Lab

Find the Lab in detail here

Content:

  • intro to scales (with pictures) slides
  • live coding demo: high buildings
  • live coding demo: dead celebrities

Assignments:

Due Monday (2020/03/16):

  • Be prepared to explain your Data Zine project to me in 2 minutes. Prepare visuals (your paper prototype, your in-progress coding, and more if necessary) to support your explanation.
    • On Monday we will be chatting in a one-on-one setting to understand where you are in your process and what your needs are going forward.

Due Wednesday (2020/03/18):

  • Aim to upload a finished version of your Data Zine project.
    • Make notes of difficulties you still need to resolve.

Week 5

prediction and uncertainty

Prediction & Uncertainty

one-on-one check ins, each 4.6666666667 minutes ((75mins class time - 5 mins intro time)/14 students):

  • Iris
  • William
  • Daisy
  • Kris
  • Novia
  • Ellen
  • Kenneth
  • Lydia
  • Thea
  • Sumner
  • Lishan
  • Crystal
  • Yufeng
  • Shiny

Wednesday Lab

Lab 5 - Review + Axis & Custom SVG Shapes

Find the Lab in detail here

Content:

  • check out WIP
  • Review and New Learnings
    • Part 1: Setup a project + filter (video 20:44)
    • Part 2: Time Scale + finding minimum and maximum value (video 26:18)
    • Part 3: Linear Scale (video 10:58)
    • Part 4: Building a D3 Axis! (video 15:09)
    • Part 5: Using custom SVG Shapes (video 19:51)
  • Question

lab5

Saturday Lab

Lab 6: Enter, Update, Exit, Transitions

Find the Lab in detail here

  • In the lab, we are building this visualization:
    • preview

Assignments:

Due Monday (2020/03/23):

  • Please record a video presentation of you project.
    • The video should be 2.5 minutes long.
    • The video should be a screen capture in which you flick through the pages of you zine.
    • As you show the pages, discuss the following questions - feel free to add other things you want to say, or change the order, be yourself :)
      • What data did you choose to collect?
      • How did you collect it, what was the routine?
      • Why did you choose to visualize the data in this way?
      • What can be seen in the visualization? Does it reveal something you didn't expect?
      • Did you make crucial compromises? Which ones?
      • If this project had a larger scale and wasn't built for print, how would you imagine it to be?
    • When you are done, drop the video into this folder and add a link to the class wiki (no worries, only nyu emails can see the Google Drive).

Week 6

Data Zine Project Presentations

Monday Class

During the Presentations, please help each other by giving feedback, thoughts, ideas, inspiration in this document.

Presentation Order:

  • Iris
  • William
  • Kris
  • Lydia
  • Yufeng
  • Shiny
  • Daisy
  • Sumner
  • Kenneth
  • Lishan
  • Ellen
  • Thea
  • Crystal
  • Novia

Assignments:

Due Wednesday (2020/03/25):

  • Find two articles that tell a story with data and data visualization.
    • Supply links to them in a markdown file alongside 2-3 sentences explaining what they are about. Push the file and add a link to the class wiki.
    • Be prepared to explain what you enjoy about these articles and the way they use data / data visualization.
    • finding your own sources is highly encouraged. Nevertheless, here are potential sources: nytimes, washingtonpost, fivethirtyeight, pudding.cool.

Due Monday (2020/03/30):

  • This weeks coding exercise is described in detail in Lab 6
    • do you best solving the exercise, then post a link to the class wiki.

Week 7

Surveillance Capitalism I

Wednesday Class

Assignments:

Due Monday (2020/03/30):

  • Take a deep dive into the datasets that you can find online.
    • Find three different datasets that you like, write a short paragraph highlighting what they are about and how you could imagine building a project around them.
    • collect those notes in markdown file, push to your repo and submit a link to the class wiki.
    • Find some sources for datasets in our Resources Page.
      • if you find other cool sources (both english and chinese), consider submitting them to a collection I have started 😊
      • Dedicate time to this research, find something that you feel connected to and inspired by --> you will spend about seven weeks dealing with the subject you choose, pick something exciting.
      • What matters is your passion for the subject as well as the potential for creative visualizations of it.
      • keep your mind open to the possibility of collecting or scraping an interesting dataset - if this is something of interest to you, I will assist and advise you.

Due Wednesday (2020/04/01):

  • Prepare a very short presentation about your favorite of the three subjects your pre-selected.
    • Each of you will present their topic in this format:
      • 5 slides that can only contain images (no text!)
      • 20 seconds per slide as you talk along explaining your interest and what you hope to make visible through your project
      • practice your text and its timing (email it to me by Tuesday if that helps)
      • this is a VERY short presentation, do not invest more than maximum (!) 2 hours to prepare it.
      • Add your 5 slides to this Drive Folder and add a link to the class wiki by Tuesday night, please.
  • "Surveillance Capitalism"
Optional / Further Reading:

Monday Lab

Lab 7: More Practice: Enter/Update/Exit Selections

Find the Lab in detail here

  • In the lab, we are building this visualization:
    • preview

Assignments:

Due Monday (2020/04/06):

  • Listen to two podcasts and write 200-300 words for each along the below prompts
    • "Radio Lab Right to be forgotten"
      • try to come up with a set of guidelines how public records/data should be dealt with -- and/or discuss the difficulties of this task.
      • can you think of other situations for similar kinds of dilemmas?
    • "The Daily The End of Privacy as we know it?"
      • weigh the benefits and risks of a system like ClearView
      • which aspects do you find particularly problematic & how might we regulate undertakings like this?
  • add your response to the class wiki

Due Wednesday (2020/04/08):

  • Finish the Lab's Coding Exercise.
    • You showed be feeling comfortable with all the techniques used in it,
    • Experiment
    • add your finished work to the class wiki

Week 8

Surveillance Capitalism II

Wednesday Class

Looking Ahead:
  • next week:
    • one class & two labs (wednesday, saturday)
  • the week after:
    • Contextual Report Writing due
    • Contextual Report Presentations

If you imagine your final project, all the text bits, the meat, the stuff that actually tells a story and guides the viewer through your visualization/webpage/experience should be done in 2 weeks (as part of your Contextual Report). Therefore, see assignment:

Assignments:

Due Monday (2020/04/06):

  • Start to dive deep into the subject of your dataset. Contextually, that is. Use this structure as a pointer to guide your research.
  • Come to class with substantial starting points for each of the points you aim to cover. In class, we will be talking through your plans one-on-one. After this class, we embark on two technical labs before the presentations are due. Schedule wisely!

Monday Class

Today:

  • one-on-one check ins
    • each 4.6666666667 minutes ((75mins class time - 5 mins intro time)/14 students):
      • Shiny
      • Yufeng
      • Crystal
      • Lishan
      • Sumner
      • Thea
      • Lydia
      • Kenneth
      • Ellen
      • Novia
      • Kris
      • Daisy
      • William
      • Iris

Assignments:

Due Monday (2020/04/13):

  • You picked your dataset. Now get deeply familiar with its context. Follow instructions in the brief and the writing guide.
  • Add your written report to this folder by Monday 20/4/13 noon and post a link to the class wiki.
  • Prepare a 4-6 minute presentation.
    • if you plan to not use Google Slides, please let me know by Friday
    • add your slides to this folder by Monday 20/4/13 noon and please add a link to them to the class wiki, too.
      • pro tip: avoid too much text on your slides, it really does make them less clear. slides do not need to speak for themselves but illustrate what you have to say.
  • Thanks! You got this!!

Week 9

Ethics & Privacy

Ethics & Privacy

Wednesday Lab

Lab 8: Generators, Components & Layouts

Find the Lab in detail here

  • In the lab, we learn about the line generator.

Assignments:

Due Saturday (2020/04/11):

  • Modify the code from the Lab's video to build the below interaction:
    • coding exercise
    • upload your result and post a link to the class wiki.

Saturday Lab

Lab 9: From Line To Map

Find the Lab in detail here

Assignments:

Due Wednesday (2020/04/22):

  • Set yourself a visual goal for a map exercise (e.g. I want a dot to move from location to location).
  • This is not a "project", but an exercise and opportunity to create something visually pleasing.
  • Below are examples.
    • play with different projections.
    • try to transition between different projections.
    • try to transition between differen geojson data sets.
    • be playful.
  • Submit you work to the class wiki when you are done.

lab9

Week 10

Data Story: Contextual Report Presentations

Data Story: Contextual Report Presentations

Presentation Day 1:

  • Iris
  • Sumner
  • Kenneth
  • Thea
  • Lishan
  • Ellen
  • William

Help each other with feedback here: FEEDBACK DOCUMENT

Due Monday (2020/04/20):

* Logic Magazine is great. This article is from their issue on China - if you are interested in getting access to more articles from this one, please let me know :)

Presentation Day 2:

  • Novia
  • Lydia
  • Kris
  • Crystal
  • Shiny
  • Daisy
  • YufenX

Today's feedback goes here: FEEDBACK DOCUMENT

Week 11

Power & Control I

Power & Control I

Monday Class

  • Discussion of the reading.
  • What is a data story?

Due Wednesday (2020/04/22):

  • Create a detailed paper prototype for your final project.
  • Scan it (or take very high resolution photographs in good light), then add a link to the class wiki.

Due Monday (2020/04/27):

Wednesday Lab

Lab 10: interaction

Find the Lab in detail here

Due Wednesday (2020/04/29):

  • Finish your data story website.
    • Build everything out except (!) the visualizations (keep placeholder divs).
    • Think of the structure, various pages.
    • Importantly: think of the contextual information you need (mostly taken from the research you already did)
  • Submit you work to the class wiki.

Week 12

Power & Control II

Power & Control II

Monday Class

one-on-one check ins, each 4.6666666667 minutes ((75mins class time - 5 mins intro time)/14 students):

  • Ellen
  • Kenneth
  • Lydia
  • Thea
  • Iris
  • William
  • Sumner
  • Lishan
  • Crystal
  • Yufeng
  • Shiny
  • Daisy
  • Kris
  • Novia

Wednesday Lab

Lab 11: Scrolling & Force Layouts

Find the Lab in detail here

Due Wednesday (2020/05/06):

  • Finish your data story website.
    • Include the visualizations into last week's websites
    • Submit you work to the class wiki.

Week 13

Resistance

Due Monday (2020/05/11):

  • Prepare a 4-6 minute presentation of your project. Be prepared to share your screen (and sound if necessary) and demo your website while explaining it to the audience.
    • assume the audience has never seen your project (guest critics wint have)
    • don't assume that anyone reads large passages of text during your presentation - explain/summarize what we need to know
    • talk about your process, compromises you made, aspects you are most proud of as well those that didn't turn out the way you intended.
  • Your code needs to be uploaded to your repository and a link to it added to the class wiki.

Due Friday (2020/05/16):

  • Add a well-formated README.md file to your project.
  • It should include:
    • the projects name
    • a animated GIF of your favorite detail of the visualization (something that moves, a transition)
    • 2-3 sentences about the project
    • links to the source of the data you used

Week 14

Data Story: Final Project Presentation

Monday Class

Data Story: Final Project Presentations Part I

  • Presentation: 4-6 minutes
  • Feedback: 4-6 minutes

Please leave thoughts on the Feedback Document. Thank you!

Presenters:

Wednesday Class

Data Story: Final Project Presentations Part I

  • Presentation: 4-6 minutes
  • Feedback: 4-6 minutes

Please leave thoughts on the Feedback Document. Thank you!

Presenters:

Last words

  • version of your project that I will grade should be
    • in your original calss repo ("my-cdv....")
    • linked to in the class wiki
    • this FRIDAY (2020/05/15), 2pm
    • please see below mini requirements
  • class photo
  • the show
  • your were a pleasure to work with, thank you for taking part in this class :)

Due Friday (2020/05/16):

  • Add a well-formated README.md file to your project.
  • It should include:
    • the projects name
    • a animated GIF of your favorite detail of the visualization (something that moves, a transition)
      • you can include images in a README file using ![name of image](path-to-image.gif)
    • 2-3 sentences about the project
    • links to the source of the data you used

Course Overview and Learning Outcomes

The overarching goal of this course is for students to gain the tools and the comfort to think critically about the ways data is utilized in the ever-growing technological landscape we are immersed in. With this in mind, the course is split in two weekly sessions: a theoretical class and a practical lab.

The classes include lectures introducing contemporary theorists, artists, groups, and in-class discussions or exercises. Themes guiding this exploration include “Human / Bias”, “Prediction & Uncertainty”, “Surveillance Capitalism”, “Ethics & Privacy”, “Power & Control” and “Resistance”. In the weekly lab, students will learn the fundamentals of web-based data visualization using JavaScript. The purpose of this is to understand what data feels like through hands-on experimentation and what data says or doesn’t say by rendering the information it carries visually.

Upon Completion of this Course, students will be able to:

  • map actors, their roles and relations within a broader data infrastructure.
  • identify problematics of "datafication" and generate ideas for response.
  • identify various visions, values and cultures inherent to datasets.
  • build data visualizations for the web.
  • build their own datasets.
  • make use of data APIs and scraped data.
  • visually communicate information pertaining to a given dataset.
  • critique their own work and others' constructively.

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a course designed for the undergraduate students at Interactive Media Art (IMA), NYU Shanghai

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