the talk will start by explaining certain episodes on a way from the halfspace depth in multivariate location (``the tukey depth") through depth in general data-analytic situations (models?) toward the psychedelic experience of a new notion of depth in the location-scale model, location-scale depth, and its most tractable version, the student depth. the latter has a couple of entertaining theoretical and computational properties, stemming from the fact that it is nothing but the bivariate halfspace depth interpreted in the poincar\'e plane model of the lobachevski geometry - in particular, invariance with respect to the m\"obius group and favorable time complexities of algorithms. the practical implications involve a new fancy location-scale typical value, the student median, as well as somewhat extravagant graphical tool for exploring distributional properties of univariate samples, a sort of cousin to the quantile-quantile plot. \vskip .1in \noindent however, perhaps more than those particular accomplishments it may be worthy to note potential new views on data and questions that the process raises: the role of invariance (if any) in data analyses, whether there can be such a thing as median in sophisticated situations, and, more generally, whether classical rank-based nonparametrics can be elevated beyond their traditional (essentially) univariate setting; and also, whether we may be missing yet some brave new data worlds in the realm of non-euclidean geometry.