Artificial Intelligence Techniques in Data Science: Trends, Challenges, and Future Directions

Abstract

The concept of Artificial Intelligence (AI) has been incorporated into data science, allowing one to analyze large and complex data to take action. This review examines the basic AI concepts which are machine learning, deep learning and reinforcement learning and how they are used in prediction, classification, and decision-making. The new trends in this area like AutoML, Explainable AI, Edge AI, and integration with big data and IoT are mentioned, and the problem of data quality, model interpretability, scalability, and ethical issues are also addressed. Lastly, the future directions point at sophisticated algorithms, AI-based decision support, the integration of quantum computing, and governance models, which reflects the radical scope of AI in the formation of data-driven innovation.