Data are predominantly becoming the main source of information to understand real-world phenomena and describe their behavior. Therefore, engineers should master extracting relevant information from data. This course provides the theoretical and methodological foundations to characterize measured signals (data) as random (stochastic) processes, understand the main principles and features of the latter, ultimately showing how these tools enable the extraction of estimates for unknown parameters or other stochastic signals (estimation and filtering). The goal of the course is thus to enable students to proficiently extract ("learn") relevant information from data.