We present spectral methods for numerically estimating statistical properties of uniformly-expanding Markov maps. We prove bounds on entries of the Fourier and Chebyshev basis coefficient matrices of transfer operators, and show that as a result statistical properties estimated using finite-dimensional restrictions of these matrices converge at classical spectral rates: exponentially for analytic maps, and polynomially for multiply differentiable maps.Our proof suggests two algorithms for the numerical computational statistical properties of uniformly expanding Markov maps: a rigorouslyvalidated algorithm, and a fast adaptive algorithm. We give illustrative results from these algorithms, demonstrating that the adaptive algorithm produces estimates of many statistical properties accurate to 14 decimal places in less than one-tenth of a second on a personal computer.