This brief explores data science methods that workforce programs can use to predict participant success. With access to vast amounts of data on their programs, workforce training providers can leverage their management information systems (MIS) to understand and improve their programs' outcomes. By predicting which participants are at greater risk of dropping out of their program and why, providers can segment their caseloads so that participants receive services better tailored to their needs. Within the data science field, machine learning (ML) has gained popularity for its ability to extract hidden patterns without being explicitly guided by a data analyst. While these data science methods hold promise, are the added costs and complexity worth it? The authors explore the tradeoffs by answering the following questions: (1) What factors are important in predicting a participant's outcome in a program?; (2) Are participant outcomes predictable using simple methods, like creating basic risk indicators in Management Information Systems (MIS)? For example, how well does an indicator for prior education predict participant outcomes?; and (3) What is the added value and cost of incorporating regression and more complex machine learning methods?