This paper provides an overview of modal analysis techniques used in fluid dynamics, including proper orthogonal decomposition (POD), balanced POD, dynamic mode decomposition (DMD), Koopman analysis, global linear stability analysis, and resolvent analysis. These techniques are used to extract physically important features or modes from flow fields, enabling the analysis of complex flows in a low-dimensional form. Modal decomposition is a mathematical technique to extract energetically and dynamically important features of fluid flows. The spatial features of the flow are called spatial modes, accompanied by characteristic values representing energy content or growth rates. These modes can be determined from flow field data or governing equations. Data-based techniques use flow field data, while operator-based techniques use discrete operators from Navier-Stokes equations. The paper discusses eigenvalue and singular value decompositions, which form the basis for modal decomposition techniques. It also covers pseudospectra, which reveal the sensitivity of eigenvalue spectra to perturbations. The paper presents various modal decomposition methods, including POD, which extracts modes based on optimizing the mean square of the field variable. The snapshot method is used for large data sets, reducing the size of the correlation matrix. SVD is related to POD and can be used to determine POD modes. The paper also discusses the strengths and weaknesses of POD, including its ability to capture kinetic energy and its limitations in handling higher-order correlations. The paper provides illustrative examples of POD applied to turbulent separated flow over an airfoil and compressible open-cavity flows.This paper provides an overview of modal analysis techniques used in fluid dynamics, including proper orthogonal decomposition (POD), balanced POD, dynamic mode decomposition (DMD), Koopman analysis, global linear stability analysis, and resolvent analysis. These techniques are used to extract physically important features or modes from flow fields, enabling the analysis of complex flows in a low-dimensional form. Modal decomposition is a mathematical technique to extract energetically and dynamically important features of fluid flows. The spatial features of the flow are called spatial modes, accompanied by characteristic values representing energy content or growth rates. These modes can be determined from flow field data or governing equations. Data-based techniques use flow field data, while operator-based techniques use discrete operators from Navier-Stokes equations. The paper discusses eigenvalue and singular value decompositions, which form the basis for modal decomposition techniques. It also covers pseudospectra, which reveal the sensitivity of eigenvalue spectra to perturbations. The paper presents various modal decomposition methods, including POD, which extracts modes based on optimizing the mean square of the field variable. The snapshot method is used for large data sets, reducing the size of the correlation matrix. SVD is related to POD and can be used to determine POD modes. The paper also discusses the strengths and weaknesses of POD, including its ability to capture kinetic energy and its limitations in handling higher-order correlations. The paper provides illustrative examples of POD applied to turbulent separated flow over an airfoil and compressible open-cavity flows.