Quantum Computing in Data Science marks the convergence of two cutting-edge technological realms, heralding a new era of computational capabilities. This paper delves into the transformative impact of quantum computing on data science, exploring how quantum algorithms can solve complex data science problems faster and more efficiently than classical computers. Quantum computing's potential to process vast datasets and perform complex calculations rapidly addresses significant challenges in fields such as cryptography, optimization, and molecular simulation. We discuss the principles of quantum computing, including quantum bits (qubits), superposition, and entanglement, and how these principles enable quantum computers to perform multiple calculations simultaneously. The paper examines the current state of quantum computing, its integration in data science, the challenges it faces, such as error correction and quantum decoherence, and the future prospects of this technology. By offering a comprehensive understanding of quantum computing's role in data science, the paper aims to provide insights into its potential to revolutionize data analysis and decision-making processes.