What are the key differences between data science and operations research, and how do these differences impact their respective applications in solving complex problems?

Data science focuses on extracting insights from large datasets using statistics, machine learning, and programming, emphasizing prediction and pattern recognition. Operations research applies mathematical modeling, optimization, and decision analysis to improve processes and resource allocation. While data science Excels in forecasting and trend analysis, operations research is stronger in structured decision-making, making them complementary in tackling complex problems.