In the past several years kinematic data sets from Milky Way satellite galaxies have greatly improved, furthering the evidence that these systems are the most dark matter dominated objects known. This paper discusses a maximum likelihood formalism that extracts important quantities from these kinematic data sets, including the amplitude of a rotational signal, proper motions, and the mass distributions. Using a simple model for galaxy rotation it is shown that the expected error on the amplitude of a rotational signal is∼0.5 kms−1with∼103stars from either classical or ultra-faint satellites. As an example Sculptor is analyzed for the presence of a rotational signal; no significant detection of rotation is found, with a 90% c.l. upper limit of∼2 kms−1. A criterion for model selection is presented that determines the parameters required to describe the dark matter halo density profiles and the stellar velocity anisotropy. Applied to four data sets with a wide range of velocities, models with variable velocity anisotropy are preferred relative to those with constant velocity anisotropy, and that central dark matter profiles both less cuspy and more cuspy than Lambda-Cold Dark Matter-based fits are equally acceptable.