Health disparities are a recognized public health concern and the need to address these disparities remains worthy of bringing new methods that assist in closing the gap. This research examined the effectiveness of data science to highlight health disparities, and to convey the value of data science applications in related health care applications. The goal of this research was accomplished by undertaking a multi-phased and multi-method approach, best represented in three individual essays. In essay one, a systematic literature review assessed the state in current academic literature of data science applications used to explore health disparities and to determine its applicability. The systematic review was guided by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Essay two assessed the capacity of data science software to address the effectiveness of these data science technologies in examining health disparities data. This was conducted using KDnuggets data pertaining to analytics, data science, and machine-learning software. The research in this essay demonstrated the potential utility of leading software to perform the kinds of data science operations that can achieve improved care in healthcare networks by addressing health disparities. Essay three provided an appropriate case study to showcase the value data science brings to the healthcare space. This study used a geographic information system to create and analyze choropleth maps to determine the distribution of prostate cancer in Texas. SPSS software was used to assess the social determinants of health that may explain prostate cancer mortality.