Abstract
This study targets organizations in the data science domain seeking optimal platforms for their operations. It employs experimental research using the comparison method and mathematical modeling integrated into the Global Utility Method and TOPSIS Method. The aim is to create hierarchies of three prominent data science platforms: IBM Watson Studio, SAS – Advanced Analytics Software and Google Cloud AI Platform. The analysis was conducted using Microsoft Excel. Various comparison criteria are meticulously applied to ensure mathematical correctness and comprehensiveness. The Data Science Platforms Conclusions, drawn from individual case studies, indicate that different methods yield similar results due to algorithmic variations and data sensitivity. Minor changes were observed in hierarchy positions, attributed to coefficient importance and method sensitivity. There is a necessity for multiple platforms to meet diverse organizational needs. Continuous development and innovation are crucial in this evolving field, where user preferences and technological advancements drive platform suitability.