Nowadays, we are experiencing a growing interest in data science, a relatively new discipline at the intersection between Statistical Learning, Engineering and Operations Research in which practitioners develop and use techniques and algorithms to extract useful insights from an increasing number of huge collections of data. However, the real challenge is not only to find proper ways to deal with the volume of data but also be able to cope with their velocity, variety, veracity and value (the so-called 5 Vs of Big Data). The majority of the datasets are characterized by a large number of high-dimensional patterns, such as those found in genetics, chemistry, finance etc. Others are also characterized by a high level of noise or missing values.