Using a longitudinal national educational dataset, data science methods were applied to explain students' educational trajectories and determine the most predictive variables in STEM degree attainment. Challenging the notion of the STEM pipeline, an Alternative Pathways to STEM (APS) model was proposed. Using a foundation of Social Cognitive Career Theory, academic, demographic, and economic variables were selected for analysis and Imbalanced dataset and random forest data analytic techniques were implemented. Results supported the consideration of the Alternate Pathways to STEM model given the varied educational pathways identified throughout STEM matriculation. Additionally, multiple academic variables, except for career interest, were found to be most predictive of STEM graduation, while demographic and economic variables were not significant. These findings have important implications for recruiting STEM students and increasing STEM graduation rates. Additional research is recommended using these data extraction and analysis methodologies on other datasets to further test the efficacy of the Alternative Pathways to STEM model.