A Framework for Automated Insights: Exploring AI-Driven Data Science Techniques

Abstract

This systematic review explores the role of Artificial Intelligence (AI) in driving data science innovations, focusing on the diverse AI techniques employed to extract automated insights from large datasets. The paper examines machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL) as core AI-driven methods that have revolutionized industries such as healthcare, finance, manufacturing, and marketing. Through a detailed literature review, the paper highlights the benefits of AI in improving operational efficiency, predictive analytics, and decision-making accuracy. However, the review also addresses the challenges posed by the implementation of AI in real-world applications, including issues related to data quality, ethical concerns, interpretability of AI models, and the risk of algorithmic bias. The paper emphasizes the importance of developing transparent, fair, and accountable AI systems to mitigate these challenges. The findings suggest that AI-driven data science techniques have the potential to redefine how organizations leverage data for decision-making, but they require careful consideration of ethical implications, data integrity, and model transparency. Finally, the review suggests areas for future research, particularly in Explainable AI (XAI), algorithmic fairness, and the integration of AI with emerging technologies like edge computing and autonomous systems.