Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs in order to tune or correct them. This work presents a set of simple techniques that, if applied to readily available historical academic data, could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths, and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum redesign.