The differences between data mining and data analytics lie primarily in their objectives and processes. Data mining refers to the process of discovering patterns and extracting valuable insights from large datasets by using algorithms and statistical methods. It emphasizes pattern recognition and often involves generating predictive models. In contrast, data analytics focuses on interpreting the insights derived from data, providing context, and assisting in decision-making. It usually utilizes the findings from data mining to develop actionable strategies. While both fields intersect, data mining is more about the exploration phase, while analytics emphasizes application and insight utilization.