Knowledge discovery in large and complex datasets is one of the main topics addressed by the so-called ``Data Science'' but is also a topic of main interest for the ``Science of Knowledge'' (or Artificial Intelligence). Indeed data and knowledge are interacting and knowledge discovery is applied on datasets and has a direct impact on the design of knowledge bases (or ontologies). Following this idea, it can be interesting to have at hand a generic formalism that can support knowledge discovery and, as well, knowledge representation and reasoning. Accordingly, in this presentation, we introduce Formal Concept Analysis (FCA), a mathematical formalism for data and knowledge processing. FCA starts with a binary table composed of objects and attributes and outputs a concept lattice, where each concept is made of an intent (i.e. the description of the concept in terms of attributes) and an extent (i.e. the objects instances of the concept). Intents and extents are two dual facets of a concept that naturally apply in knowledge representation. There are two main variations of FCA, Relational Concept Analysis (RCA) for dealing with relational data and Pattern Structures (PS) for dealing with complex data (numbers, sequences, trees, graphs). We will discuss the usability of FCA and its variations in knowledge discovery and knowledge engineering through various tasks and applications, such as e.g. data and text mining, information retrieval, biclustering and recommendation, and extraction of functional dependencies. Finally, the structure of a concept lattice can be visualized and allows a suggestive interpretation for human agents while it can be also processable by software agents.