What is the key difference between data analytics and data mining?

The distinction between data analytics and data mining is rooted in their respective purposes and processes. Data analytics primarily involves the examination of data to identify patterns, draw conclusions, and make informed decisions based on insights derived from that data. It includes statistical analysis, predictive modeling, and various quantitative techniques to extract meaningful information from datasets.

In contrast, data mining is the process of discovering patterns and knowledge from large amounts of data. It typically focuses on the exploratory aspect, employing algorithms and techniques to identify previously unknown patterns, relationships, or trends within the data. This can include methods such as clustering, classification, and association rule learning.

Understanding this difference clarifies why the correct choice highlights that analytics is concerned with analyzing data patterns, while mining is oriented towards the collection and initial exploration of raw data.