Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim. Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the underrepresentation problem by identifying what has worked (leveraging existing real-world data) to increase representation. This work studies the educational pathways of persons who have successfully transitioned into the computing workforce and identifies the common roadmaps that have contributed to retention, persistence, and success in attaining computing employment. Descriptive statistics, Logistic regression, Classification algorithms, Clustering, and Predictive analytics were employed, using the Stata statistical tool and Orange Data Mining tool on real-world data, to identify educational pathways that have resulted in successful employment outcomes for women and blacks in computing. The results of this analysis have highlighted key information that is capable of informing future “Broadening Participation in Computing” (BPC) efforts. This is because the information will enable researchers and decision makers to have a clearer picture of what educational choices have resulted in favorable outcomes for underrepresented minorities and women in computing; and consequently, researchers and decision makers would be able to more accurately target their BPC efforts to achieve optimal results. This knowledge can also be applied in career advising for young students who are trying to chart their path into computing, providing insight into alternative pathways.