Research experiences for undergraduate students (REUs) in the fields of water and energy not only enhance student learning but also bolster their self-efficacy to conduct independent research, provide valuable networking opportunities, and guide career pathways, especially for students from underrepresented groups. Data management skills are crucial in this context, as students need to navigate and analyze large volumes of data efficiently. The FAIR principles – Findability, Accessibility, Interoperability, and Reusability – provide a framework for promoting good data management practices and maximizing the value and impact of digital assets across various domains. While the FAIR principles are gaining relevance across STEM fields, research about teaching and learning processes that facilitate the adoption of these principles remains limited. To address this gap, the Justice in Data Bootcamp, was launched in the Summer of 2023 at the University of Texas at Arlington. This innovative one-week online program introduced undergraduate students to Python programming, FAIR data principles, and machine learning. The curriculum was designed to be interactive, incorporating hands-on activities, mentoring experience, and a symposium where students presented their research projects. This study evaluated the bootcamp's impact on students' FAIR-related skills. We used paired t-tests to examine changes in pre- and post- familiarity, confidence, and conceptual knowledge, alongside workshop evaluations using descriptive statistics for a cohort of n = 13 students. Preliminary findings showed statistically significant gains in students' familiarity and confidence. However, students' conceptual scores were statistically significantly lower by the end of the bootcamp. This may have resulted from the structure of the questions. Overall, the bootcamp received positive feedback from participants. Further qualitative analysis of student presentations will continue to inform these results.