Description: There is a growing demand from public and private policymakers and funders to apply big data science and machine learning for evaluation. The demand is growing due to public awareness of how the private sector uses machine learning algorithms to create on-demand tools that cost-effectively augment human planning, assessment, prediction, and decision-making.
In this introductory course, participants will learn the fundamentals of integrating the theory, methods, and machine learning algorithms of big data science into their evaluation approach. This will include an introduction to Bayesian theory, machine learning algorithms, predictive and prescriptive analytics, causal modeling, and addressing selection and algorithmic bias. The course will guide participants through an interactive step-by-step process of building evaluation models using primary and secondary datasets. Recommended Audience: This course is best suited for mid to late-career evaluators with experience conducting quantitative and mixed methods evaluations, especially preparing and analyzing primary and secondary datasets using analytic software packages like SPSS, SAS, and Stata.