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A Philosophy of Data

Instructor: Mimi Onuoha
Term: Fall 2017
Time: Tuesdays, 2:10pm - 6:00pm
Course Number: DA 2132, 4 credits
Office Hours: Wednesdays, 12pm - 1pm (in Pod or room E214)

Course Description

We live in a world where more data has been and is being collected than ever before. But what does that mean? What information can we glean from the data? How do we represent what is being collected, and more importantly, what is missed?

This intro-level course examines the emergent fields of data collection, analysis, and visualization from an art perspective, asking how the technologies inherent to each can be leveraged for response, creation, and critique.

This course is equal parts technical (programming-based) and conceptual (writing/reading/discussion). Students will learn the basics of data gathering and analysis with the programming language Python, as well as learn to create data visualizations and manifestations using various computational tools. In addition, students will be introduced to the technical ways in which data is stored, collected, and consumed, as well as gain an understanding of the historical and critical positioning of the current “data revolution."

Expectations and Requirements

  • Class attendance and punctuality
  • Participation during class discussions and presentations
  • Weekly assignments + accompanying blogposts
  • Midterm and final project (completed individually or in groups) + online documentation

This class is an inclusive and harassment-free space for everyone, with no tolerations of discimination based on gender, race, sexual orientation, religion, disability, or appearance. Please feel free to let me know privately if you have an academic accommodation.

All students are allowed a maximum of two absences. It is worth stressing that because the class covers so much new material, it is very much to your advantage to not miss any classes, if possible.


Final evaluation will be based on the completion of all assignments, blogposts, class participation, quality of work, and attendance.

For homework assignments, it is more important that you put forth effort and attempts to understand and experiment with the material than it is for you to create a 100% successful project (particularly for first-time coders). Everyone gets one free pass for turning in an assignment late, but only one.

For the midterm and final projects, I am more interested in what you choose to do than in what you can do. We will eleaborate on these distinctions in class.


The course will cover a number of different themes that intersect with issues of data collection. Because each theme consists of both a technical and conceptual idea, most themes will span two weeks.

Each class will have the same structure: the first portion of class will be devoted to reviewing homework. The next will consist of a critical reading, viewing, or discussion. The last will consist of a coding exercise or technical workshop (I will screen-record the technical sections of class so that you can refer to them outside of class). The portions of class devoted to each of these will differ depending on the week.

For some weeks, there will be additional links and resources provided. It is not necessary for you to look at these, however, it would certainly be interesting to look at them and think through connections to the subject matter of the class.

Though we will meet in a lab that has computers, it may be beneficial to bring your laptop with you to class.

I can be reached via email at all times, but I am very slow to respond to emails sent over the weekend. If you are in need of a prompt response, please email during the week. I reserve 24 hours to respond, but typically will reply much sooner.


Week One: September 5
Introductions to course, logistics, data, and each other


  • Read through this up until the section that begins with "Structure of UNIX commands". If you're feeling ambitious, you can follow along and try some of the commands.
  • Maciej Ceglowski's "The Internet With A Human Face"

Week Two: September 12
Archives, Collections, Memory, part 1: A World Remembered
Data begins with storage, collection, memory. In this class, we'll talk about what that storage looks like physically, how we reconcile with a world where (nearly) everything can be saved, and what forms data can take. Technical exercise: introduction to the command line via git.


  • Read through this.
  • Derrida's Archive Fever (found in "Readings" folder)

Week Three: September 19
Archives, Collections, Memory, part 2: Deleting, Erasing, Removing
[Fundamentals of programming in Python]
This week's screen recording.


  • Read this. Do you think that the right to be forgotten and a right to information are at odds with each other? Post a response.

Week Four: September 26
Web Scraping and the World As Computer
Considering the world as a data source.


Week Five: October 3

Aesthetic and Representations
Representing and manifesting data.

Week Six: October 10
Python review and working on midterms

Week Seven: October 17
Midterm Presentations


Week Eight: October 24
What does it mean to render ourselves as datapoints? How do we carry out, and what is achieved, through self-quantification?


Week Nine: October 31
We are increasingly living in a surveillance state, and data plays a crucial role in facilitating it. What are technical avenues for response, resistance, and complicity?

Week Ten: November 7
*Guest presentation - TBA

November 14
Plan Day--No Class

[start thinking of final project ideas]

Week Eleven: November 21
Algorithms + data-driven advertising, part 1
What is the relationship between the reward system of the web and the everyday algorithms that are used to further it?


Week Twelve: November 28
Algorithms and Fairness
How can we make sense of and respond to algorithmic bias? What does fairness look like in an age of automated decision-making? Who should be concerned with this question?


Week Thirteen: December 5

Futures: Speculation, Machine Learning, Artificial Intelligence

Week Fourteen: December 12
Final Project Presentations


Philosophy of Data | Bennington College | Fall 2017






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