I'll discuss designing a socially-conscious and socially-critical data science course. This talk will be interesting to anyone who designs or delivers educational opportunities for young data scientists. The topic is important because data science is not value neutral. Learners should understand how data and data analyses can encode biases and assumptions. Particularly when we collect data from or about people, we should ask who is privileged or disadvantaged by how we gather and analyze that data? Attendees will learn how I’ve come to design and teach data science courses that emphasize morality and ethics. They’ll also learn how I design for inclusion and work to create reflective learning environments.
Brian's career has spanned teaching, researching, and being a practicing software engineer. His Ph.D. in Computing Education Research focused on how engineering students learn software design. In subsequent post-doctoral research positions, his work expanded to educational games, ethnography of engineering practice, and teaching computational modeling and data science. He then developed enterprise level web apps for educational clients as well as personally developing and releasing Transcriptase, a free qualitative data analysis app available on both Windows and macOS. Outside of work, Brian enjoys improv, games of all kinds, and being inspired by others’ favorite forms of geekery.
