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Accurate information about tire cornering stiffness is essential for the implementation of advanced vehicular control systems. Data-driven modelling method leverages the availability of high-quality measurement data alone, without vehicle parameters, which provides a tutorial to reconstruct the system dynamics and estimate tire cornering stiffness. As such, we collect the states and inputs of the vehicle to build its state space using the dynamic mode decomposition (DMD) method. Then, based on the entries of the system and input matrix, the tire cornering stiffness can be further identified by solving the linear equations via orthogonal regression with considering the measurement noise. The sufficient and necessary rank condition for the DMD execution is also analyzed. Additionally, we introduce two alternative ways to update the system and input matrices - recursive least squares (RLS) and sliding window (SW).