At the heart of every Data Science project exists the planning, design and execution of experiments. Such experiments aim at understanding the data, potentially cleaning it and performing the necessary data analysis for knowledge discovery and decision-making. Without knowing the experimental design processes that are used in practice, researchers may not be able to discover what is really hidden in their data. The first aim of this course is to look at existing experimental designs that take into account the questions that need to be answered as well as the nature of the data and the different parameters used by algorithms.

Subsequently, the course will introduce different qualitative and quantitative methods to assess the quality of the results.

All concepts will be accompanied by examples and the students will have practical exercises and a project in which they will demonstrate their knowledge.