Behavioural Data Science

Description

Behavioral Data Science (BDS) is a relatively new discipline that involves the analysis of behaviorally defined variables as they arise in large datasets ("Big Data"), typically gathered using modern digital technology (e.g., online or through mobile devices) and analyzed with techniques for detecting patterns from high-dimensional data to facilitate understanding, prediction, and control of human behavior (e.g., machine learning). The current course offers an introduction to BDS from the unique perspective of psychology, leveraging both novel contributions (network analysis, artificial intelligence, and predictive algorithms) and classical strongholds of psychological science (latent variable models, selection theory, actuarial versus clinical prediction). Objectives: After completing Behavioural Data Science, students can paraphrase assumptions behind commonly used operationalisations of psychological constructs (e.g., networks versus latent variable models, types versus dimensions) evaluate predictive algorithms for their efficiency and quality (e.g., in terms of sensitivity, specificity, and bias) engage in scientific thinking to differentiate between different uses of computational models (e.g., prediction versus explanation of human behaviour)