Engage in Exploratory Factor Analysis (EFA) to identify underlying factors within your data. This course is intended for AI and Data Science professionals seeking a robust understanding of latent variables, factor rotation, and extraction. This includes the concept of latent variables, factor extraction and rotation, score estimation, and the difference between EVA and Confirmatory Factor Analysis (CFA). Model fit indexes such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), SRMR, and RMSEA will be reviewed. Participants will learn how Principal Component Analysis (PCA) can be used to optimize feature extraction. The course finishes with a review of use cases that are particularly beneficial for EVA, including the methodology for explaining the findings to executives. This course should be followed by the Confirmatory Factor Analysis (CFA) course to ensure a robust understanding of the two methods. - Course is free for Gold and Elite Tier members - 60-75 minutes of learning - Participants must pass the assessment to earn a digital badge and certificate