Below are the details for the Data Metrics and Visualization II class I am teaching in the Spring of 2020 at the School for Visual Arts in New York. The full syllabus can be found here.


Class: Jan 14th — Apr 28th | Tues 6–7:30 pm at SVA DSI
Instructor: Kalli Retzepi ( ) (pronouns she/her)
Office Hours: email me!


During this class you will:

  • Get comfortable with various data collection techniques
  • Learn about web technologies that underlie these data collection techniques (you don’t have to become experts at them)
  • Critique the politics of data collection, sharing and consequences
  • Synthesize the above in a final project


The most important thing to know about data, its abundance and accessibility, is that it’s neither the ‘new oil’ (despite that it has made a few people ridiculously rich), neither is it the fuel of technodystopia. As most things in the history of humanity, it sits somewhere in the middle. My goal is to first and foremost present data collection and manipulation tools that you will most likely encounter as you progress in your career, but also to foster a discussion and an awareness about aspects of the data-driven universe you might not yet be fully aware of. My belief is that there is nothing as important as thinking critically, particularly about technology.

To this end, the class will follow a slightly unusual format: until Spring break, classes will be spent talking about data and data collection as tools that we can use, and understand how to use them. Once that’s done, we will reverse course and engage in a critical examination of the same tools.

What we will cover (first half of semester)

  • How to collect data
  • How to think critically about data
  • Go over different storytelling techniques (mainly inspired by existing projects)

The second half of the semester will synthesize what we learned in a final project, and complement that with some storyboarding, prototyping and group crit activities.

What we won’t cover, but I wish we had the time to (Please do ask me for more resources if interested!)

  • Interface critique
  • Deep learning
  • AI
  • Hacking
  • Ethics
  • Feminism/Gender studies


1. In-class participation and attitude Your contribution is necessary to make the course successful for everyone - if you don’t go over the readings we will be discussing in class it will be obvious!

2. Critical discussion (first half of semester)

During the first half of the semester, students will close each class with a 30 minute presentation on topics of their choice in response to each week’s readings. You can choose one of the readings/case studies for each topic listed in the Resources section below (usually it’s good to read a few of the readings on a single topic, but that’s up to you). On Mondays by EOD, you will submit a short critical reflection in the form of a blog post about your topic (I will provide guidance and tips on how to do that). During class, you will facilitate a 30 minute discussion around it.

I think this works your reading, writing, synthesizing and presenting skills - all of which are important and I hope will complement your thesis work.

3. Final Project (second half of semester)

During the second half of the semester (after Spring Break), we will break up in teams and work on a single project until the final two weeks, which we will use for presentations.

You are designers, so I am quite confident that the final result will be aesthetically pleasing. However, this is an opportunity to dig deep in terms of process, storytelling, intentionality and critique. Very loosely speaking, a project will start with some sort of data collection (can be very basic, even analog), followed by analysis and finally a presentation. Ideally you will draw inspiration from the topics covered during the readings for the class. You can use this time to work on a part of your thesis if you want to.

Here are some project ideas, but feel free to diverge as widely as you want:

  • A website that keeps track of how energy-costly it is to host itself
  • A web experience that changes characteristics (e.g. its color palette) according to the sentiment detected in Trump’s daily tweets
  • A zine where you document one week of your personal data of choice à la ‘Dear Data’
  • A data-driven typeface
  • A data-driven audio or video experience