The goal of this course is to give participants a first gentle introduction and solid conceptual grounding in what has been called ‘data science’, i.e. experimental work that is data-driven and empirical. The focus is on methodology, defining an experimental protocol, devising hypotheses, thinking about measuring success, but also on more practical approaches like basic machine learning methods (both supervised and unsupervised) and the introduction to popular tools. The course also demonstrates some practical applications of the techniques shown, and deepens the students' skills via practical exercises.