The emerging field of data science has brought attention to how we teach statistics and data science (Bargagliotti et al., 2020; Franklin et al., 2007) and prepare the next generation of statistics and data science teachers (Franklin et al., 2013). To realize the full potential of statistics and data science, researchers have also called for using data to guide conversations about race and racism (Philip et al., 2016, 2017), especially given the Black Lives Matter movement, climate change, and public health. In this dissertation, I drew on the Teaching Mathematics for Social Justice (Gutstein, 2006), Quantitative Critical Race Theory (Castillo & Gillborn, 2022; Crawford et al., 2018; Gillborn et al., 2018), and Habits of Mind (Cuoco et al., 1996) frameworks to study the potential of using a social justice-oriented approach to teaching data science for preservice mathematics teachers, highlighting the intersectionality of race and racism with statistics and data science. Data comes from a credit-bearing course taken by 14 students during the Summer 2021 term at a four-year public institution in the US-Mexico borderlands of Southern California. Data included pre- and post-assessments, pre- and post-task-based interviews, and classroom data (e.g., student work, whole-class recordings, field notes). There were four research questions and analyses in this dissertation. First, there was a qualitative description of the features used to design the course centered around Freire’s (1998) notion of critical consciousness and praxis with illustrations of how the design features were enacted. Second, there was a quantitative analysis of pre- and post-assessments that aimed to measure the students’ statistical and data scientific content knowledge. Third, there was a qualitative analysis of pre- and post-task-based interviews that aimed to capture development of students’ critical statistical and data scientific practices. Finally, elements of a focusing phenomenon framework were used to coordinate how aspects of the classroom environment (e.g., design features, tasks, tools, and the teacher) directed students’ attention towards understandings of race and racism in the context of data science.