INTEGRATION OF STATISTICAL ANALYSIS AND ARTIFICIAL INTELLIGENCE IN ENVIRONMENT OF COMPUTING IN CLOUD: APPLICATION ON GOOGLE COLLAB

This research investigates the application of Artificial Intelligence (AI) techniques and descriptive statistical analysis using Google Colab, a cloud computing platform that facilitates the execution of Python codes. The theme explores the feasibility of using this tool to analyze large volumes of data, highlighting the advantages of integrating AI and data analysis. The central problem lies in adapting traditional statistical models to massive data environments, ensuring the accuracy and reliability of the results. The overall objective was to analyze how the use of Google Colab and Python libraries can improve the performance of descriptive statistical analyses. The research adopted the Giftedean neoperspectivist paradigm, integrating the theories of Popper, Morin and Gardner, with the application of the hypothetical-deductive method and techniques such as PCA, clustering, and cross-validation. Among the main findings, the efficiency of Google Colab for data processing and interpretation stands out, enhancing the analysis of complex patterns. The gaps found include the need for adjustments in predictive models and the importance of data preprocessing. The research contributes theoretically by deepening the understanding of the integration of AI and statistical analysis, methodologically by offering a replicable framework, and empirically by proposing good practices for data analysis. The study adds value by demonstrating how cloud computing tools can democratize access to advanced methods, promoting more detailed and accessible analysis.