This chapter provides a comprehensive guide for implementing a wide range of DEA models in Stata, bridging theoretical concepts with practical applications. It begins with classical CCR and BCC radial models, extends to the non-radial and slack-based measure, and incorporates undesirable outputs for robust environmental efficiency assessment. Advanced models, including the DDF and non-radial DDF (NDDF), are covered alongside environmental performance indices such as UEI, EEPI, and TCPI. Dynamic performance measurement is addressed through the MPI and its environmental extension, the ML index, including meta-frontier and GTFP decomposition. The chapter also details statistical inference methods for DEA, including bootstrap corrections, independence tests, and returns-to-scale tests. Finally, it demonstrates two-stage DEA regression analysis, integrating Tobit, Truncated, and Simar-Wilson approaches to examine determinants of efficiency. With annotated code, worked examples, and step-by-step implementation, this chapter equips readers with the skills to operationalize a full suite of modern DEA techniques—static and dynamic, radial and non-radial, environmental and productivity-oriented—enhancing their ability to conduct rigorous efficiency and productivity analysis in applied research.