The research examined naive user analysts' learning of data analysis skills; namely. (1) the difficulty of learning data analysis, (2) the differential learning rates among development tools, and (3) the dimensions of the tools contributing to the learning differences. A total of fifty-six students participated in two experiments. The experiments involved repeaied trials of practice and feedback in drawing application-based data models. On average, the participants were experienced end users of computer systems in organizations. The two tools examined in the experiments were the logical data structure model (LDS), which is based on the entity-relationship concept, and the relational data model (RDM). The correctness of the models improved over the trials in both LDS and RDM groups with LDS users performing better than RDM users, particularly in terms of representing relationships. LDS users were found to be more top-down motivated in their method of analysis than RDM users. The study suggests that among end users, the LDS formalism is more easily learned than the RDM formalism. The results also imply that end-user training should stress conceptual top*wn analysis, not bottom-up output directed analysis.