The course 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. This course will introduce machine learning algorithms for structured (quantitative, ordinal, and categorical) and unstructured (qualitative text) data modeling, including how to train machine learning algorithms to support conducting a mixed methods evaluation.
For text analytics, participants will learn about natural language processing (NLP) algorithms that are used to improve the breadth and depth of qualitative analyses while significantly reducing the time it takes.
The course will use an open-source, no-cost, no-code (knowledge of R or Python is not required) visual-based analytics platform – KNIME – and will introduce participants to its suite of analytic tools and machine learning algorithms.
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.