In the last decade, analyzing and modelling data-driven dynamical systems has emerged as a key topic within the scientific community. Generally, the function f that describes the dynamics of most dynamical systems is characterized by only a few active terms. From this perspective, SINDy algorithm (Sparse Identification of Nonlinear Dynamics) plays a [3] crucial role in identifying an appropriate linear combination of these terms in a given library constructed from data measurements. In [1, 4], SINDy has been extended to study stochastic systems described by Itō SDE, where both drift and diffusion are computed using Kramer-Moyal approximations based on data. In this work [2], we show a preliminary version of Stochastic SINDy with some applications of the algorithm to low-dimensional problems. Finally, we analyze the behavior of the approximation errors in estimating the drift and diffusion functions with respect to both the number of SDE realizations and the sampling time points. This Minisymposium falls within the activities of PRIN-MUR 2022 project 20229P2HEA "Stochastic numerical modelling for sustainable innovation", CUP: E53D23017940001, granted by the Italian Ministry of University and Research within the framework of the Call relating to the scrolling of the final rankings of the PRIN 2022 call. References [1] L. Boninsegna, F. N¨uske, C. Clementi, Sparse learning of stochastic dynamical equations, J. Chem. Phys., 148(2018). [2] . Breda, D. Conte, R. D’Ambrosio, I. Santaniello, M. Tanveer, Sparse Identification of Nonlinear Dynamics for Delay and Stochastic Differential Equations, “9th European Congress on Computational Methods in Applied Sciences and Engineering”, Lisboa, Portugal, 3-7 June 2024, Session: MS178 - Numerical Modeling and Data Analysis for Advancing Sustainable Innovation. [3] . L. Brunton, J. L. Proctor, J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, PNAS, 113(2016), pp. 3932-3937. [4] M. Wanner, I. Mezi´c, On Numerical Methods for Stochastic SINDy, 2023.