ENHANCEMENT THE ANALYSIS OF DATA WITH AI: ONE EXPLORATORY APPROACH USING GOOGLE COLAB

This research investigates the potential of Artificial Intelligence (AI) in realizing exploratory statistical analysis through the Google Colab platform. The growing demand for accessible and collaborative tools for analyzing large volumes of data motivated this study, which search respond as the AI integration and analysis of data he can optimize the understanding of sets complexes of information. THE The general objective is to explore the use of AI and machine learning techniques for exploratory analysis, using Python codes. Adopting the giftedean neoperspectivist paradigm, which allows the coexistence of different truths, the research triangulates theories of complexity, systems and machine learning. The hypothetical-deductive method guided to the steps of formulation and test of hypotheses, using techniques such as descriptive analysis, linear regression and clustering. The main findings indicate that the integration of libraries such as Pandas and Seaborn facilitates the visualization of patterns and the interpretation of data, while the use of machine learning enhances the identification of complex relationships. The main gaps involve the computational limitations of Google Colab for the analysis of large volumes of data and the need for more advanced visualization techniques. The contributions include a theoretical model that connects AI and exploratory analysis, a practical methodological guide for researchers, and a set of replicable empirical practices. This research adds value by making data analysis techniques accessible to a wider audience, promoting the democratization of science and education.