Data science is an interdisciplinary field that applies scientific methods, processes, algorithms, and systems to extract meaningful insights and knowledge from structured and unstructured data. At the core of this field lies the utilization of mathematical algorithms to analyze vast datasets and make informed decisions. These algorithms, infused with statistical techniques, computational models, and predictive analytics, play a crucial role in understanding patterns, predicting trends, and making data-driven decisions. Data science utilizes mathematical tools, algorithms, and techniques to extract meaningful insights from data, make predictions, and drive decision-making processes. This article explores the strong relationship between data science and mathematics, delving into various mathematical concepts and methodologies employed in data science, and highlighting their applications and contributions to the field. Basically, there is a lot of confusion about using and implementing perfect modeling algorithms in data science. In this article, the utilization of different machine learning and Artificial Intelligence algorithms applied to data science are described. Moreover, in this article we try to explore the concept of performance efficiency in data science algorithms, highlighting the factors that influence efficiency, discussing common metrics for evaluation, and presenting optimization techniques to enhance algorithm performance.