The divide between those who do and those who do not excel in mathematics is patterned in problematic ways. Women and people of color are typically underrepresented in Science, Technology, Engineering, and Math (STEM) and other quantitative fields (ex. Finance) where mathematics plays gatekeeper. However, mathematics is not a subject these groups of people are somehow less capable of learning (Chesnut et al., 2018). Instead, this imbalance points to issues within the education system where only a narrow group of students' needs are being met, constituting a history of institutionalized sexism, racism and classism. The current U.S. math education system seems to value a narrow and antiquated set of skills which necessarily result in only a small group of students succeeding at the highest levels. Students spend their time learning to reproduce a list of methods and procedures that have been in place since the 1800's even though this type of work can be done more quickly and accurately by an average smart phone (Education Association, 1894; Wolfram, 2020). Meanwhile the real world is bursting with data, and students rarely learn how to make sense of it or wield its power. The ability to analyze and make sense of complex data is a skill that can help solve problems on a global scale including issues faced by the environment, world health, the economy, and more. Further, students are tasked with making sense of data on a regular basis as technology has become a part of daily life. While technology has automated many jobs, only humans can think critically about contextual factors and circumstances to inform interpretations of mathematical models and data analyses. This type of creative mathematical thinking is what students need to be learning in the 21st century and the implementation of this change constitutes the focus of this dissertation study. The field of data science education is at an especially critical point in its expansion as secondary schools are beginning to offer formal mathematics courses on the topic. Researchers posit that the real world and authentic nature of the content will invite a wide range of student interests, and a widening of opportunities to find belonging in mathematics (LaMar & Boaler, 2021). However, being that the field of data science education research is still in its infancy, very little is known about how students experience data science learning. This mixed-methods dissertation study focuses on how an experience in a high school data science course led students to shift their feelings of belonging in STEM and other quantitative fields. Participants in this study came from Willow High School, located in a suburb of the San Francisco Bay Area and include all consented students enrolled in Ms. Weber's 12th grade Data Science course as well as Ms. Weber herself. The data that informs this study consists of 25 days of classroom observation, pre- and post-course student surveys, a series of interviews with Ms. Weber as well as a series of interviews with the enrolled students conducted at the beginning, middle, and end of the school year. The student participants include all 20 enrolled students with documented consent to participate, with an in-depth look at the data associated with 12 focal students. Quantitative methods were applied to the student surveys including two sample t-tests assuming unequal variance as well as a matched pair two-sample significance tests on a subset of the data (Coladarci et al., 2010). Qualitative coding methods were applied to both the teacher and student interviews, starting with open coding and ending with a series of themes (Miles et al., 2018). Results of this study show that both the students and the teacher had qualitatively different experiences in the data science course compared to past high school mathematics courses. Not only was the content in the data science course more authentic and relevant to student interests, but these factors also shifted the ways in which students experienced challenge and struggle. In past math classes, many of the students reported struggling to memorize material or find right answers quickly and felt ashamed or embarrassed for not meeting these expectations. In contrast, in the data science class, students struggled to learn new technology and to situate the findings from their analyses within the broader context of the data but students expected to struggle in these ways because the course was new and novel for both the teacher and the students. For the students, struggling in data science looked and felt very different than it had in past math classes resulting in a more judgment-free learning experience. Related to the findings uncovering the qualitative differences between data science learning and past high school math learning were results showing a shift in how agency and authority functioned in the data science classroom. A number of factors came together that allowed for both the students and the teacher to share disciplinary authority in the data science classroom including the authenticity of the curriculum content paired with Ms. Weber's work to teach the class using a student-led pedagogical approach. The positioning of the data science course as a terminal course for grade 12 students freed Ms. Weber to integrate a student-led approach without fear of outside pressures like a high stakes end of course exam. Students were active participants in the learning process, making decisions for the direction of their work and providing support to one another. Through this shared agency and authority the function of student status and collaboration shifted. In past math classes, students were well aware of those who held high academic status and this impacted the quality of students' collaboration in negative ways where lower status students felt even more ashamed for asking for support. Academic status functioned very differently in data science because at any given time a wide range of students were able to contribute their thinking and reasoning about the challenges students were tasked with completing. Being that data science was a new course at Willow High School, no one student had significantly more experience or expertise in the content compared to another. For this reason, students were able to engage in supportive and effective peer collaboration. Finally, students showed increased feelings of belonging in their data science course as well as STEM and quantitative fields more broadly after completing the course. The factors that made data science learning different from past math learning (i.e. relevance, different experiences and interpretations of struggle, shared agency and authority, leveled academic status and supportive collaboration) impacted students' expressions of self-efficacy and confidence in data science and beyond. The majority of students showed a shift towards greater interest in pursuing careers that would require them to work with numbers, for example STEM careers or careers in other quantitative disciplines (i.e. Business) indicating an increase in students' feelings of belonging in those spaces. Altogether, the results of this study have the potential to impact secondary mathematics content and coursework as well as secondary mathematics teaching. This course served to broaden students' perspectives of how mathematics applies in the real world resulting in a wide range of students finding belonging, self-efficacy, and confidence in STEM and other quantitative fields. These findings point to the importance of updating K-12, and especially secondary mathematics to reflect the ways in which this learning can be applied in the real world and within contexts that students find personally valuable. Furthermore, these findings also provide evidence for the need to support teachers to integrate shared agency, and authority with students through student-led classroom approaches. Together, these changes have the potential to support a wide range of students--especially in terms of gender and race--to find belonging in STEM and other quantitative fields. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]