Developments in data science and related fields have created plentiful technologies for the business world. The rapid developments in new technologies obstruct analytics teams' data science technology identification, selection, and management processes. Utilizing an improper set of tools, teams encounter problems and inefficiencies when managing their technology architecture. We undertake this problem by providing a technology selection approach synthesizing quantitative multi-criteria decision-making methods with qualitative group decision-making approaches. The purpose of this study is to develop a comprehensive technology selection methodology for Data Science that satisfies principles for developing a strategic technology management toolkit. Following the principles for developing technology management tools, the proposed methodology enables the decision-makers to compare the data science tools and select the most suitable alternative according to their data science project requirements. The practitioners can identify the technology categories, functional and non-functional requirements, differentiating features, and possible technology stacks. While the existing studies in this domain consider the technology selection problem in isolation and investigate a subset of technologies, our approach considers the end-to-end data science processes from the technology management perspective to provide data experts with a systematic approach exposing integrable technologies.