This article introduces data-driven feedback linearization for nonlinear systems with periodic orbits in the zero-dynamics, a challenging scenario for data-driven control design due to higher-order terms of internal dynamics acting as disturbance inputs to the controllable subsystem. The design includes a data-driven feedback linearization-based controller and a two-part estimator for reconstructing unknown nonlinear terms in the normal form of a nonlinear system. The effects of coupling between subsystems in the closed-loop nonlinear system are explored, revealing that such coupling prevents asymptotic convergence of controllable states. Additionally, it is demonstrated that the estimation error in controllable states scales linearly with the sampling time. Simulation-based validation of the proposed data-driven feedback linearization is presented.