Becoming a Data Scientist: Understanding the Data Science Identity Formation in a Data Science Program

This lightning talk presents early work surrounding undergraduate students' experiences in data science and computing classrooms that shape their identities. Identity in learning spaces has been studied extensively in STEM disciplines, but little research has focused on undergraduate students majoring in data science. Since data science sits at the intersection of several disciplines, such as mathematics, statistics, and computer science, it is unclear how data science students navigate those individual discipline identities and/or construct a unique identity that is greater than the sum of those parts. As Michigan State University's Computational Mathematics, Science, and Engineering department offers data science degrees, it is of particular interest to understand the classroom messaging and experiences that shape our students' sense of self in relation to data science and computing. The project will build on various identity frameworks understand how students' data science identities are developed or stifled through classroom experiences. From previous research, we know that prototypical identities of STEM center cisgender, heterosexual, white male students and can exclude students from different demographics and lived experiences. To address this, I will utilize a critical lens to interrogate possible inequity, bias, and inaccessibility that may emerge from students' identity shaping experiences in these classrooms. At the time of this lightning talk, a thematic analysis of the first seven interviews I have conducted will be presented. All are welcome to attend the presentation and provide feedback and insight on initial observations, synthesis, and plans for the future.