We discuss our experience in teaching data analytics, and in particular prescriptive analytics, to students in business schools using an inter-coherent case study, where a managerial decision is decomposed into a series of research problems with interlocking analyses and the outcome of one analysis is the input of other analysis. Students who focus on data analytics are often familiar with statistical theories and statistical software packages (e.g. R, Matlab, SAS, etc.), but often have a weak background in business theory, survey research, and application of models to address real-world business scenarios. Other business students, in contrast, are often weaker in statistical theory, computational techniques, and programming, but more practiced in generating creative business insights. Prescriptive analytics requires the integration of both skill sets to identify the optimal actions for decision problems. We propose a new inter-coherent case study that mixes lectures on theory and hands-on lab-sessions involving analysis and programming that can be helpful to students learning of data analytics through a series of interlocking hands-on exercises. Our empirical study finds that the proposed case study positively contributes to students' analytic abilities across courses and universities. Implications, limitations, and future research are discussed.