Paper Details

The convergence of Data Science and Artificial Intelligence (AI) has revolutionized the ability to derive actionable insights from large, complex datasets, transforming industries ranging from finance to healthcare. This paper explores the integration of AI-driven methodologies into data science workflows, emphasizing how machine learning (ML) and deep learning (DL) techniques can enhance data analysis, pattern recognition, and predictive modeling. We examine the role of supervised and unsupervised learning algorithms in data preprocessing, feature engineering, and model selection, as well as the impact of neural networks on unstructured data such as images, text, and audio. Additionally, the paper discusses the challenges and limitations of AI in data science, including model interpretability, bias, and scalability in real-world applications. Through case studies in areas like healthcare diagnostics, fraud detection, and natural language processing, we demonstrate the practical benefits of AI-driven data science, highlighting improvements in predictive accuracy, decision support, and automation. We conclude by addressing the ethical considerations in deploying AI-powered data science models, stressing the importance of transparency, fairness, and accountability in the age of intelligent systems.

Data Management, Data Visualization, Machine Learning, Applications.