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Critical Data & Visualization Fall 2019

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

  • Instructor: Leon Eckert, [email protected]
  • Class Times: Mondays & Wednesdays, 1:15pm-2:30pm
  • Credits: 4
  • Room: 824
  • Office Hours: Tuesdays, 9am-12pm (sign up)
Other:

Course Description

Data is at the heart of the increasing role technology has in our lives. Data collection and algorithmic processing are not only central to recent technical breakthroughs such as in AI 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, or influencing the things we buy, the news we read or even the people we date. 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 class seeks to untangle some of these issues practically and theoretically.

Course Overview and Learning Outcomes

Content

Week Class (Monday) 👾 Lab (Wednesday)
1 Mapping the Landscape Collect Data
2 Data Infrastructures Meet D3js I
3 Bias Meet D3js II
4 Prediction and Uncertainty D3 Scales
Fall Break
5 Concept Review Old bits, new bits: scales, data functions, axis, filter method
6 Data Zine Project Presentations 🎉 Introducing: Time. Update transitions
7 Data Ethics Enter, Update, Exit
8 Concept Review Generators, Components & Layouts
9 Automating inequality From Line to Map
10 Data Story: Work in Progress Presentations 📚 Interaction: Event listeners in D3
11 Surveillance Capitalism
12 Concept Review
13 Activism, Leaks and Whistleblowers
14 Data Story Project Presentations 🥂

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 and Data Infrastructures.
    • looking beyond data as a resource
    • data is performative
  • “data”
    • from latin (‘given’)
      • how about "capta" (== ‘taken’)?*
    • used in singular and plural
  • data has no truth *J Drucker

Assignments:

Due this Wednesday (2019/09/04):

  • 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 (2019/09/09):

  • Listen to two parts of Reply All's 'The Crime Machine' Podcast. This is both super entertaining AND relevant next class. You can listen to the podcast via
  • 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'"? Try draw parallels to the CompStat system from the Reply All Podcast.
    • 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.
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 this Wednesday (2019/09/11):

  • 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 room (I can help distribute))
  • 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)
  • the last two points can be worked on simultaneously (you don't need all the responses to start working on the code)
  • relvant links:

WEEK 2

data infrastructures

Monday Class

Reading discussion

Mapping Data Infrastructures test!

Announcement: Data Zine todo sample projects!

Introduce Group Research Assignments. The teams are

  • Eszter & Robert
  • Yiqi, Phyllis & Cyndi
  • Jerry, Kevin & Jinzhong
  • Kennedy, Sarah & Thomas (added here)
  • Aleksandra & Jannie (Thomas missed this)
slides

Assignments:

Due Wednesday (2019/09/11):

Due Monday (2019/09/16):

*tips:

  • use Dear Data (link1, link1) as an inspiration
  • what to collect? anything, the more detail the better.
  • don’t just decide on what general subject to collect, but make a plan for the actual measurements (think of Dear Data)
  • you can use a notebook, or spreadsheet, or build you own Google Form that you open up every evening/now and then/hour/minute.
  • set an alarm for measurements.
Optional/Related readings and resources:

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
    • Create the "canvas" (it's an SVG)
    • Bind elements to data
    • Get data
    • Data function

Assignments:

Due Wednesday (2019/09/18):

  • Read the notes from the lab carefully.
  • 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

bias

Monday Class

Presentation and discussion by Eszter & Robert.

Check in on data self-collection.

Bias group activity*

Pick next group research

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

Assignments:

Due this Wednesday (2019/09/18):

  • Data Self-collection for this week's lab
    • Save the data you have collected thus far, the temporal data for the data zine in a json format.
    • push it to your GitHub and post the link to the class wiki.
    • if the data is sensitive and you don't want it online, please share it, or a description of it with me in an email ([email protected]).

Due Monday (2019/09/23):

  • Read The Minority Report by Philip K. Dick
    • Think of contemporary applications in which data is used to predict the future and we then act upon. Compile a list of 3 such situations that come to your mind, push them to your Github and share the link in the class wiki.
  • Groups research only
    • Aleksandra, Jannie and Robert, find the resources for your group research here.
Optional/Related readings and resources:

Wednesday Lab

Find the Lab in detail here

Content:

  • useful resources
  • incoming data: the enter-selection
  • repeat: data function (demo coding)
  • modify elements
    • multiple attributes
    • classes
  • grouping elements
  • text

Assignments:

Due Wednesday (2019/09/25):

  • 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 asspects 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

prediction and uncertainty

Monday Class

Assignments:

Due this Wednesday (2019/09/25):

  • Read the following Chapters of The Visual Display of Quantitative Information by Edward R. Tufte:
    • Graphical Excellence
    • Graphical Integrity
    • Sources of Graphical Integrity and Sophistication
    • I highly recommend getting a physical copy from the library. A digital version is also available on request.
  • Paper prototype for your Data Zine Project:
    • Print out this template on A3-sized 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 (2019/10/07):

  • Enjoy this over the fall break.
  • Read the chapter "Data Visualization" from Technologies of Vision : The War Between Data And Images by Steve F. Anderson.
    • There is a copy in our libray.
    • A digital version is also accessible to NYU students. Please contact me if you run into difficulties.
Optional/Related readings and resources:

Wednesday Lab

The Lab

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

WEEK 5

concept review

Monday Lab

The Lab

Putting together what we have so far learned to produce a high quality, well coded D3 graph. Topics specifically reviewed:

  • JS filter function
  • d3.timeParse
  • d3.min, d3.max, d3.extent
  • scales
    • d3.scaleTime
    • d3.scaleLinear
  • axis
  • graph

Assignments:

Due this Wednesday (2019/10/09):

  • Aim to be close to finished with your Data Zine project.
  • Prepare a short presentation (2 minutes) of your project that you will pitch to me. We will then talk about it together and I'll give you last-minute advice in a one-on-one setting.
  • Before next Monday's class, I will print your project. Please deliver it on time.
  • DATA ZINE DUE DATE:
    Monday, 2019/10/14, NOON

Wednesday Class

One-on-one reviews of projects progress.

  • Eszter
  • Jinzhong
  • Robert
  • Eric
  • Thomas (not in class)
  • Sarah
  • Cyndi
  • Kennedy
  • Phyllis
  • Jannie
  • Yiqi
  • Aleksandra
  • Jerry (not in class)

Assignments:

Due Monday (2019/10/14):

  • Finish your Data Zine Project and add a README.md file to the project folder.
    • In the README.md, please describe your project in a few sentences and include screenshots of each of the pages. Feel free to expand on points you want to highlight or talk about challenges you faced and solutions you found (or compromises you made) while working on it.
  • Push the project to you GitHub repository and submit a link to the class wiki by
    Monday, 2019/10/14, NOON 🚨
  • This is a hard deadline; I will try to print out all zines before class. 🤞
  • If you are are finished earlier, please, please notify me in an email. This will allow me to print your project before the deadline and make things easier/possible.
  • Don't hesitate to email me any questions that come up.

WEEK 6

data zine project presentations

discuss these questions for 3-4 minutes:

  • 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?

then, 2 minutes feedback from the guest critics.

Assignments:

Due Monday (2019/10/21):

  • Listen to: Radiolab: Right to be Forgotten (WNYC, Apple Podcast, Spotify). It's a very nice podcast, enjoy it, and make your own thoughts about what's a fair or right use of data in the described situations.
  • 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.
  • Next week's Group Research Presentation:
    • Eric, Jerry & Jinzhong
    • find the resources for your group research here.
Random thoughts or references

Wednesday Lab

The Lab

Assignments:

Due Wednesday (2019/10/23):

WEEK 7

week7.jpg

Monday Class

  • Ethics: presentation and discussion by Eric, Jerry and Jinzhong.

  • Discussing Radiolab: Right to be Forgotten.

  • Go through submitted articles

  • Project Announcement: Data Story Project (Week 14) and Work in Progress / Context Presentation (Week (10)

    • The final project is a story that emerges from and is told alongside a dataset (or several).
      • Inspiration can be found in the articles you collectively gathered this week.
      • To tell the story you will have to become deeply familiar with a dataset that you choose (or create): not only with its technical structure, but also its context, its origin, the information it does/does not carry, the insights it provides and controversies it might fuel.
    • You will present the contextual research part of this project in a 5 minute Work in Progress / Context Presentation (using slides) in Week 10. You are expected to demonstrate your research abilities, not your technical expertise. What is the story that you are telling?
    • After that, as your final project (Week 14), you will create a multi-page website: your Data Story. It must include at least two interactive data visualizations as well as contextual information (mainly from your Work in Progress presentation).

  • The Plan

    • Pre-select datasets
      • due this Wednesday
      • see this week's assignment section below.
    • Commit to a dataset
      • this will happen next Monday.
    • Contextual Research
      • presented in Week 10.
    • Build of a web based Data Story
      • presented in Week 14.
random question:

who has used Node before?

Assignments:

Due this Wednesday (2019/10/23):

  • 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 by Wednesday.
    • Find some sources for datasets in our Resource page.
    • Dedicate time to this research, find something that you feel connected to and inspired by --> you will spend about five weeks dealing with the subject you choose, pick something exciting.
    • 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.
Optional/Related readings and resources:

Wednesday Lab

  • Dynamic Visualization, Part 2 (advanced)
  • The three different selections
    • Update
    • Enter
    • Exit

The Lab

Assignments:

Due Wednesday (2019/10/23):

  • Read carefully through the Lab, maybe multiple times.
  • Finish the website we have started to build in class making all the buttons functional
    • the last button is a wild one, make it to unexpected things
    • when you are done, push and submit to the class wiki
  • when you have mastered it

lab7assignmentsmall

WEEK 8

week8.jpg

Monday Class

One-on-one reviews of the concepts for the next project.

  • Yiqi
  • Sarah
  • Eszter
  • Jinzhong
  • Cyndi
  • Aleksandra
  • Jerry
  • Eric
  • Jannie
  • Thomas
  • Kennedy
  • Phyllis
  • Robert

Assignments:

Due Monday (2019/11/04):

  • Read The Messy Truth About Social Credit from Logic Magazine's China Issue (recommended beyond this particular reading).

    • Note down your thoughts and opinions to discuss them next class.

  • Prepare a short presentation about your project's subject for next classes:

    • Each of you will present their topic in this format:
      • 5 slides that can only contain images
      • 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 Sunday if that helps)

  • Next week's Group Research Presentation:

    • Kennedy, Sarah & Thomas
    • find the resources for your group research here.
Optional/Related readings and resources:

Wednesday Lab

  • Review "Enter, Update, Exit" Coding exercise
  • Generators, Components & Layouts
  • Line Exercise

The Lab

Assignments:

Due Wednesday (2019/11/06):

  • Create a "quick and dirty" visualization using the data you have chosen to use for your project.
    • The focus of this exercise is to make your data workable.
    • Include at least one transition.
    • Be as simple or fancy as you desire in the design of your visualization.

WEEK 9

week9.jpg

Monday Class

Assignments:

Due Monday (2019/11/11):

  • On Monday, Novebmer 11th, your Data Story: Work in Progress Presentations is due.
    • you picked a dataset, for next week, research thoroughly about its context, its origin, the information it does/does not carry, the insights it might provide and controversies it could fuel.
    • prepare a 4 minute presentation
      • take this as a guide (see also below gif)
      • if you plan to not use Google Slides, please let me know by Friday
      • share a link to your slides with me by 11/11 noon.
      • 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.

guide-to-wip.gif

Optional/Related readings and resources:

Wednesday Lab

  • Important Info for next week's Data Story: Work in Progress Presentations
    • Significance
    • Next Semester this assignment will include a written part. This semester it will not because I want to stick to the syllabus. However, the structure of the (future) writing assignment should help you to know what I expect you to cover.
  • Check out your homework
  • Coding

The Lab

Assignments:

Due Wednesday (2019/11/13):

  • Create a paper prototype for your Data Story Project
    • Download template here: horizontal or vertical layout.
      • Print the in A3 Format.
      • You can use multiple printouts for different pages of your website.
    • Use the space around to browser for annotations and further explanations of your website.
      • this should cover interaction, transition, movements, sounds, special effects.
    • Scan the prototype when you are finished, upload it to your repository and submit a link to the class wiki.

WEEK 10

week10.jpg

Monday Class

Work in Progress Presentations! (slides)

Assignments:

Due Monday (2019/11/18):

  • Read the chapter Cloud from the book New Dark Age by James Bridle

    • Find three passages (1-3 sentences) you find interesting:

      • Quote them in a README.md file like this

        and add you own thoughts below.

    • Submit your thoughts to the class wiki.

  • Next week's Group Research Presentation:

    • Cyndi, Phyllis & Yiqi
    • find the resources for your group research here.
Optional/Related readings and resources:

Wednesday Lab

  • WIP Presentations
  • Paper Prototypes
  • What do we think a "Data Story" is?
  • Lab: Interaction
  • Upcoming Labs

The Lab

Assignments:

Due Wednesday (2019/11/20):

  • Visualize part of your data (this exercise can ultimately turn into your final project)
    • Add interaction using the techniques we learned today (excluding simple CSS hover ;-)

WEEK 11

week11.jpg

Monday Class

  • Surveillance Capitalism: presentation and discussion by Cyndi, Phyllis & Yiqi.
  • Data Story
    • Due: Monday, December 9th, noon
    • A website that guides the viewer through a subject alongside a data set. Every story has a thread, your projects needs one too.
    • The website must include at least two D3 data visualizations make use of the technical concepts covered in our labs.
    • Visualizations are complemented by text that helps guide the viewers engagement with your subject.
    • Your project must include a README.md in which your reflect on your project and process. Include visuals and cover the following questions:
      • describe what have you created?
      • how does it work?
      • how is it intended to be engaged with?/what do you hope for a viewer to take away from it?
      • which aspect of it are you must proud about, and why?
      • which part would you improve if you had more time?
      • what did your process look like? outline a rough timeline: when did you settle in on a dataset, how extensive was your contextual research and when did you start building the actual page?
      • during your process, what compromises did you make with regards to your dream outcome of this project?
  • Homework
  • Help me best tailor my material to your needs
  • Discuss the Bridle reading.

Assignments:

Due tonight (2019/11/18):

Due Monday (2019/11/25):

  • Build your website.
    • Focus on the layout, all scrolling, paging, etc. that you envision for your final project, should be included.
    • Where D3 SVGs would go, put an empty <div></div> instead and assign CSS border: solid black 1px; (to make it visible).
    • Include all of the text that will be on your final website.
    • Push and submit a link to the class wiki.
    • For next class, be prepared to talk about your ideas.
Optional/Related readings and resources:

Wednesday Lab

  • Scrolling Interactions

The Lab

Assignments:

Due Wednesday (2019/11/27):

  • to be announced

WEEK 12

WEEK 13

WEEK 14

Course Overview and Learning Outcomes

The overarching goal of this class 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 class 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. Four themes guiding this exploration are “Data, Information, Knowledge”, “Data Bias”, “Data Ethics” and “Power, Control, Access”. 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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