A series of Wilcoxon Signed Rank tests were conducted to analyze changes in students' attitudes and perceptions toward data science both in the primary cohort as well as in the secondary cohort. We assessed student's perception and attitude toward coding, data science and omics data as well as their understanding and confidence in working with this type of data and using coding tools, such as jupyter notebooks. Overall, the results demonstrate statistically significant improvements to students' understanding of and confidence with the computational analysis of biological and chemical data after completing the course with significantly large effect sizes (Table ). Some of the main perceptions and attitudes we were interested in learning about were how students understood coding, omics data, and how they felt towards coding, omics data analysis and having these concepts introduced to them in their undergraduate core courses. We found that mean scores of these attitude variables significantly increase from pre-test to post-test measures for all items except for the students' desire to have been introduced to omics in their undergraduate course. A significant increase in most of the attitude variables indicates that students, by the end of the course, felt more confident in their understanding of what omics data is and in their ability/interest to use python coding. Importantly, there was a significant difference in the attitude variable related to student's excitement to learn more about omics and data science (pre-test (M= 3.95, SD= 3.35) and post-test (M=3.35, SD= 1.167; z=-6.02, p< 0.001)). Students were asked three additional evaluative questions at the post-test measure regarding the impact of the course (Table ). Over half of all students in the cohort agreed that the course had improved their confidence in using omics data analysis.