The largest advances in understanding race and mental health have been made through the use of nationally representative survey data. The continued and expanded use of these important **data** sources are necessary for addressing acknowledged research gaps identified in this review. We also see considerable opportunities for adopting a **data** science framework to analyze **data** sources that have not been used in this line of research. For example, electronic health records (EHRs) consist of unstructured text **data** that have traditionally been examined using qualitative methods applied to a small set of EHRs. Recent advances in **data** science methods show promising results for extracting information from vast stores of unstructured text data. For example, Perron and colleagues (2019) used a **data** science methodology called machine learning methods to analyze a vast collection of unstructured text documents (>75,000) from electronic child welfare records. Their computer models extracted information on family substance abuse problems and domestic violence problems with an accuracy of more than 90% when compared to trained human coders. Human coders took over 20 min to review each document; the computer models performed the coding task on more than 75,000 records in less than 10 min.