Аңдатпа
Optimizing algorithms is essential for deriving insights from large datasets in data analytics. This thesis compares the performance of optimization algorithms using SAS Studio and Python, analyzing platform capabilities, algorithmic implementations, and computational efficiency. Key results show performance differences in methods like the Traveling Salesman Problem (TSP), Linear Regression, and K-Means Clustering; K-Means shows remarkable scalability and adaptability. For data professionals choosing ideal platforms and methods, the paper offers insightful direction. By means of effective optimization tactics, enhanced decision-making, and resource allocation, these insights can enable businesses to increase the applications of themselves. To challenge the limits of algorithmic optimization in data analytics, future studies should concentrate on include developing technology and multidisciplinary approaches.