The need for automated decision-making is steadily increasing. The goal is to derive and to implement methods for data-driven decision support for practical applications in constantly changing environments. Solving such problems requires combining data management, data science, and optimization. In general, decision problems can be described by given performance criteria, admissible decisions, constraints, and data-driven estimations of the interplay of decisions on performance. Further, every application has its own specifics, which can be exploited to solve a problem effectively. In this course, we consider different use-cases and explore suitable optimization techniques. These problems fall into the areas of (i) resource allocation management and (ii) operations management.