​There are a large number of applications which require the analysis of data not realised in the Euclidean space, but rather on some manifold (circles, spheres, hyperspheres, cylinders, torus etc.). Directional statistics constitutes a specialised branch within statistical analysis that focuses on angular observations. An inherent challenge in handling directional data lies in the non-linearity of the sample space, typically represented as a circle or circular manifold. This unique feature has garnered increased attention in the past two decades, primarily driven by the increase of data and the corresponding demand for tailored statistical methodologies. However, in many studies, the cyclicity of a directional variable is often neglected and mistreated as a linear variable. This presentation provides an overview of the world of directional statistics emphasising the need for this adapted statistical technique. The fascinating background and recent developments within this field will be explored. The usefulness of these techniques will be illustrated with some examples based in various application domains.​