A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model—a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data "as they are." The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times—a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.