智能数据科学：融合人工智能、机器学习与预测分析，支持工业5.0的数字化转型和战略决策

Abstract

Data science has become a strategic discipline for the digital transformation of organizations by providing methodologies, computational techniques, and mathematical models that transform large volumes of information into valuable knowledge for decision-making. The convergence of artificial intelligence (AI), machine learning (ML), deep learning (DL), predictive analytics, and data processing technologies has expanded the capabilities of pattern recognition, event prediction, and process optimization in business and industrial environments. Within the context of Industry 4.0 and its evolution toward Industry 5.0, these technologies are particularly relevant because of their potential to support intelligent, sustainable, resilient, and human-centric production systems.

The objective of this research is to analyze the integration of data science, artificial intelligence, and predictive analytics through a technological architecture designed to transform heterogeneous data into strategic information that supports organizational decision-making. A descriptive, exploratory, and technology-oriented research methodology is proposed, based on the stages of data acquisition, integration, preparation, analysis, predictive modeling, evaluation, and interpretation. Furthermore, a reference model is established that incorporates distributed processing technologies, machine learning algorithms, visualization tools, and data governance mechanisms.

he findings identify the main enabling technologies, establish a functional architecture for intelligent data science systems, and define indicators for evaluating information quality, predictive performance, interpretability, computational efficiency, and the practical usefulness of analytical models. Strategic applications are identified in manufacturing, healthcare, education, logistics, energy, and business administration, together with challenges related to privacy, algorithmic bias, security, and reliability.

The study concludes that integrating data science and artificial intelligence provides a technological foundation for developing data-driven organizations, strengthening analytical processes, and supporting strategic decisions. Successful implementation requires reproducible methodologies, verifiable models, appropriate infrastructure, and human oversight mechanisms that promote the responsible use of information.