This thesis addresses the challenge of selecting the most suitable computational notebook, a complex decision-making problem due to the wide variety of available platforms. For this purpose, a multi-criteria decision-making model was developed using the DEX method to systematically compare eleven local and cloud-based alternatives. The model is based on a hierarchy of criteria tailored to the needs of a technical field student, emphasizing user experience, technical features, and AI functionalities. The main contribution of this work, in addition to the decision-making model, is an objective analysis and a final ranking of the platforms. The results clearly indicate that modern cloud-based solutions such as Deepnote, Hex and Google Colab rank the highest. Local tools, particularly JupyterLab and RStudio, remain viable, but their effectiveness is primarily dependent on the user's hardware capabilities and the viability of upgrades.
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