Data Science (DS) projects aim to methodologically extract knowledge and value from data, for example to help organizations to improve performance. Dedicated process models (e. g. CRISP-DM, Microsoft TDSP) are applied to support the management of these projects. However, high failure rates in the execution of these endeavors highlight the need for improvements in the Data Science lifecycle. A crucial factor for project success persists in the adequate identification, documentation, and maintenance of requirements. Still, Data Science process models and the current state-of-the-art DS research lack a holistic set of guidelines on how to appropriately undertake requirements engineering with respect to the specific nature of such projects. Accordingly, the development of a DS requirements model including functional and non-functional requirements, that supports DS project execution needs is of particular importance. For this purpose, existing (partial) approaches from the literature from DS and related fields need to be collected by the means of a systematic literature analysis and then integrated to a holistic model. The artifacts can be evaluated using through expert interviews and/or demonstration in DS projects.