Teaching Data Science Through Art

Nettrice Gaskins
3 min readDec 15, 2020
Two type of data visualizations (my art is on the right)

This fall I’ve been working with Dramatic Results and teaching middle-school girls about data science and creativity. When introducing the topic, I use my art as an example of data visualization and predictive modeling. The former conveys or represents data sets (i.e., spreadsheets, tables) and the latter trains machine learning models and uses them to make predictions.

Data science life cycle

One of my aims is to engage students (and others) in conversations about an emerging and evolving field. But really I just want them to see algorithms and data sets as the “paint”” for their art. Here’s how I approach it:

  1. Look at different types of data sets (and code)
A set of data points to construct a bar chart. Courtesy of Processing.
This array stores the position of mouseX and mouseY values (ex. from a cursor). Courtesy of Processing.

2. Create data sets… artistically

A student’s pixel art in Google Sheets.
Another student’s pixel art.

3. Use algorithms, code, or conditional formatting (see above) to generate images or sounds using the data

Using gesture-based arrays in and with a Deep Dream (image style transfer) artwork

4. Sharing and presenting the results

Using pandemic unemployment data sets to generate music visuals.

I often move back and forth when teaching and creating artworks of my own. I also research and write about the developments in technology and the art world, which gives me a unique perspective on themes such as equity, as well as the tensions between the tech/science, art, creativity and collaboration.

Stephanie Dinkins’ “Who Are Your People?”

A few years ago I wrote an article about Shantell Martin’s art exhibition, Mind the Machine. This included the collaboration between Martin and an MIT student that, in the end, left the artist with some misgivings that was added to the article:

The most important lesson being, the fundamental action of establishing that all collaborators, essentially, come together with a goal to answer similar questions. The collaboration is a joint effort towards a similar goal. Getting to that goal requires that your creative moral standards are in alignment and that you share similar ideals when it comes to artist rights and ownership.

This last part: aligning one’s personal, or artistic standards with what STEM has to offer is something I’m going to be dealing with in 2021 when I work with a team of Black women artists and designers who will amplify cultural heritage and creativity in ways that foster community between the Black diaspora and global communities through art, design, and technology.

This is the method behind my teaching approach.



Nettrice Gaskins

Nettrice is a digital artist, academic, cultural critic and advocate of STEAM education.