Applications of linear algebra in data science

Abstract

Linear algebra serves as a foundational pillar in the realm of data science, underpinning various analytical techniques and methodologies. This research paper explores the multifaceted applications of linear algebra in the field of data science, elucidating its pivotal role in data manipulation, analysis, and interpretation. By leveraging concepts such as matrix operations, eigenvectors, and singular value decomposition, data scientists can effectively model complex systems, extract meaningful insights, and solve intricate problems encountered in diverse domains. Through a comprehensive examination of real-world case studies and applications, this paper highlights the indispensability of linear algebra in driving innovation and advancement in data-driven decision-making processes. Furthermore, it discusses the significance of understanding linear algebraic principles for aspiring data scientists to proficiently navigate the intricacies of modern data analysis tasks.