In this chapter, we look at the moments of a random variable. Specifically we demonstrate that moments capture useful information about the tail of a random variable while often being simpler to compute or at least bound. Several well-known inequalities quantify this intuition. Although they are straightforward to derive, such inequalities are surprisingly powerful. Through a range of applications, we illustrate the utility of controlling the tail of a random variable, typically by allowing one to dismiss certain “bad events” as rare. We begin by recalling the classical Markov and Chebyshev’s inequalities. Then we discuss three of the most fundamental tools in discrete probability and probabilistic combinatorics. First, we derive the complementary first and second moment methods, and give several standard applications, especially to threshold phenomena in random graphs and percolation. Then we develop the Chernoff–Cramer method, which relies on the “exponential moment” and is the building block for large deviations bounds. Two key applications in data science are briefly introduced: sparse recovery and empirical risk minimization.