The course is mostly dedicated to inference techniques that allow generalization of the conclusions derived from a sample to the population from which it came, together with an assessment of the uncertainty and the degree of precision derived from such generalization. It will mainly deal with parameter estimation procedures, either in a specific way or by means of intervals of confidence, and hypothesis testing procedures, both exemplified for some relevant cases for data-driven business management. Statistics is not about memorizing formulas or knowing how to use a calculator, but identifying which method suits best to find a solution to the problem we are facing. The course assumes a solid knowledge of mathematical analysis and understanding of Basic Descriptive Statistics. It is structured so that the students apply the theoretical concepts on a set of practical cases and a project, which allows employing all the methods and techniques learned. The objective of this course is to provide students with the necessary statistical tools to carry out a basic descriptive and inferential analysis. It will emphasize the use of the statistical programming environment R, along with its well-established and widely tested libraries for data analysis. Upon completion of the course, students will be able to understand and carry out basic techniques of descriptive and inferential statistics.