Keep It Simple: Picking the Right Data Science Method to Improve Workforce Training Programs

This brief summarizes findings and recommendations from a study designed to measure and compare the added value of models used to predict participant success within career pathways programs in the Health Profession Opportunity Grants (HPOG) Program. HPOG provided education and training in high-demand occupations in the health care field to TANF participants and other individuals with low incomes. As part of program enrollment and participation, workforce program providers have access to program data through their management information systems that has the potential to improve program outcomes through data analysis. In addition to improving program outcomes, analyzing the wealth of program data allows providers to identify participants at greater risk for program dropout and tailor the program accordingly to participants with an increased risk. From a provider perspective, predictive models vary in the level of program value, costs, and complexity. To help inform practitioner decision-making and explore differences using real-world program data, the brief explores the tradeoffs of using simple to complex data science methodology to analyze career pathways program data.