This paper introduces several methods, generally used in fluid dynamics, to provide low-rank approximations. The algorithm describing these methods are mainly based on singular value decomposition (SVD) and dynamic mode decomposition (DMD) techniques, and are suitable to analyze turbulent flows. The application of these methods will be illustrated in the analysis of the turbulent wake of a wall-mounted cylinder, a geometry modeling a skyscraper. A brief discussion about the large and small size structures of the flow will provide the key ideas to represent the general dynamics of the flow using low-rank approximations. If the flow physics is understood, then it is possible to adapt these techniques, or some other strategies, to solve general complex problems with reduced computational cost. The main goal is to introduce these methods as machine learning strategies that could be potentially used in the field of fluid dynamics, and that can be extended to any other research field.