Cancer progression is an evolutionary process characterized by the accumulation of genetic alterations and responsible for tumor growth, clinical progression, and drug resistance development. We discuss how to reconstruct the evolutionary history of a tumor from single-cell sequencing data and present probabilistic models and efficient inference algorithms for mutation calling and learning tumor phylogenies from mutation and copy number data. We present methods for integrating single-cell DNA and RNA data obtained from tumor biopsies, for detecting signatures of selection pressure in single-cell samples, and for finding common patterns of tumor evolution among patients, including re-occurring evolutionary trajectories and clonally exclusive mutations.