Issues in behavioral data science

Analyzing data about different aspects of human behavior is a valuable and challenging task and it represents the general aim of behavioral data science. Both behavioral science and data science are interdisciplinary fields. In particular, behavioral science ranges from economics and finance to psychology and sociology, up to health-related behaviors; similarly, under the umbrella of data science are statistics, machine learning, computer science, to name a few. This special issue features a range of papers that fit the above description: the contributions explore complex behaviors from multiple angles and show how behavioral data science blends elements from finance, education, healthcare, sociology, and text analysis, all through the lens of sophisticated data science methods, with a special focus on the interpretability of the results. A rough taxonomy of the articles in the special issue is: (i) contributions that tailor methods to specific application fields; (ii) contributions that present methodological enhancements to extend applicability and explainability of specific data science methods.