This thesis discusses Data Brushes, an interactive web application to explore neural style transfer using models trained on artistic data visualizations. The application invites casual creators to engage with deep convolutional neural networks to co-create custom artworks with a focus on style transfer networks created from canonical and contemporary works of data visualization and data art to demonstrate the versatility and flexibility of the algorithm. In addition to enabling a novel creative workflow, the process of interactively modifying an image via multiple style transfer networks reveals meaningful features encoded within the networks, and provides insight into the effects particular networks have on different images, or different regions within a single image. To evaluate Data Brushes, we gathered expert feedback from participants of a data science symposium and ran an observational study, finding that our application facilitates the creative exploration of neural style transfer for data art and enhances user intuition regarding the expressive range of style transfer features. This thesis explores both the practical uses of such tools for artists as Data Brushes and the interpretive uses of creating such venues for accessibility to computational art, remixing the purpose of data visualizations to be more than just graphical representations of information.