DXARTS 481 A: Data-Driven Art I

Lynn Hershman Leeson, The Electronic Diaries of Lynn Hershman Leeson 1984–2019, 1984–2019, six-channel video installation, Courtesy: the artist; photograph: Alwin Lay.

DXARTS 481 A: Data-Driven Art I

Instructor: Laura Luna Castillo

Meeting Time: MW 12:30pm - 3:20pm

Location: RAI 205 and 132

Credits: 5.0

Contact information: lcasti2@uw.edu / Raitt Hall 207/129

Office hours: MW 3:20-4:00 PM or by appointment (Raitt 129)

TA: Rose Xu

Office hours: 

Course overview

This course explores the intersection of data science and contemporary art, treating algorithms, datasets, and metadata as a primary creative medium. We will approach media (images, video, sound, and text) as indexable, searchable data within larger algorithmic systems. By investigating the creative possibilities of highly distributed data collections, students will develop dynamic, process-based approaches to composition.
The curriculum balances technical instruction in Python with critical inquiries into the ethical and aesthetic implications of data-driven art. Through a combination of theoretical lectures, archival research, and hands-on programming labs, students will synthesize algorithmic tools with artistic methodology. 

No prior coding experience is necessary. Non-STEM and STEM students welcome. 

Learning objectives:

Students will synthesize technical, historical, and critical frameworks to develop their own hybrid data-driven practice, while acquiring the technical vocabulary to troubleshoot and iterate on complex digital systems. By merging data science tools with artistic methodology, students will adapt and modify algorithmic processes to produce original creative works that critically engage with emerging culture.

Skills and outcomes include:

• Transform: Programmatically curate and remix diverse datasets (textual, visual, sonic) into original artistic media.
• Critique: Evaluate ethical biases in algorithmic structures and deconstruct the mechanisms of Machine Learning models.
• Modify: Adapt and extend existing algorithms to develop custom generative systems that align with your artistic intent.
• Troubleshoot: Apply domain-specific vocabulary to independently research, diagnose, and resolve technical challenges within your unique digital workflows.
• Synthesize: Design a hybrid research methodology that integrates quantitative data science with qualitative artistic inquiry.

Course structure and assignments:

• Most sessions consist of a lecture at the beginning of each class, which can be a discussion on readings, case-studies relating to the topics from that week, invited artist talks, and lectures. The second half of the class will connect to the lecture through hands-on practical experiments and demos. 
• The course is divided into 3 conceptual modules, each relating to specific concepts and data processes. The modules are divided as follows:

1. 1. Systems of Meaning - An introduction to key concepts, theories, philosophies, vocabulary and artworks that illustrate the use of patterns, code and software as tools that transform archives, influence our everyday and hold memories, experiences and ideologies. This module leads up to a mini-project.
   2. Oracles and translators: Text as data, Machine Learning mechanisms and as poetic agents and conduits, cultural encoders & decoders. This module leads up to a mini-project.
   3. Choreographing Complex Systems & Cybernetic Loops - data, real-time interaction, and transformation converge into a single, complex system. It serves as the synthesizing springboard for the development of the final project.

• The final project consists on creating a substantial creative and artistic project combining textual, visual and sonic archives with the data processes and concepts discussed throughout the course. The rubrics for this final assignment are based on the three modules, where each has specific conceptual prompts. The rubric should also be used as a from of inspiration and a guide to develop a critically complex creative project.

Grading structure:

• Systems of Meaning project  - 10%
• Oracles and translators project  - 10%
• Final project - 50%, consisting of:
  • Final project proposal: 10%
  • Final project development and presentation: 30%
  • Final project Documentation: 10%
• Assignments  - 20%, consisting of:
  • Homework - 6%
  • Readings - 6%
  • Lab prep - 8%
• Participation - 10%, kinds of participation encouraged:

• • • Being an active participant throughout in-class discussions and during critiques.
    • Asking questions, asking for help with the code/technology, actively and consistently discussing your ideas and your creative process. This can happen during class or approaching the instructor and/or TA during office hours or via email.
    • Attend 1-1 sessions and open studios.
    • Sharing with the class: tools, artworks, books, movies, methodologies, code... anything that is exciting or relevant to the concepts we are exploring. 
    • Telling us about topics or tools you might be interested in testing and developing a project with. 

Note regarding Multiple Submissions:

Students are not allowed to submit one project for credit in two different classes (see UW Policy on Academic Misconduct), without prior discussion with and permission from BOTH class professors. Students under these conditions will have to develop a substantial project that is deemed to be complex enough to earn credits for both classes.

Religious accommodation  

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/)Links to an external site.. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/)Links to an external site..

DEI+A statement

DXARTS strives to create a safe, affirming, and welcoming space for all cultures, races, nationalities, sexes, gender identities, ages, religions, and economic statuses. We are committed to ensuring that the University of Washington’s DEI+A values are reflected through our diverse community of students, faculty and staff, our research and public-facing events, our course curriculum, our actions, and interactions within the department. 

COVID-related policies

As per UW policy, masks/face coverings are recommended inside campus buildings - see UW policy hereLinks to an external site.. As this class is conducted in-person, students are expected to participate in class to fully benefit from course activities and meet the course’s learning objectives. Students should only register for this class if they are able to attend in-person. To protect their fellow students, faculty, and staff, students who feel ill or exhibit possible COVID symptoms should not come to class. When absent, it is the responsibility of the student to inform the instructor in advance (or as close to the class period as possible in the case of an unexpected absence), and to request appropriate make-up work as per policies established in the syllabus. What make-up work is possible, or how assignments or course grading might be modified to accommodate missed work, is the prerogative of the instructor. For chronic absences, the instructor may negotiate an incomplete grade after the 8 th week, or recommend the student contact their academic adviser to consider a hardship withdrawal (known as a Registrar Drop).