How does data exploration differ from data mining?Data exploration aims at hypothesis testingData exploration is about discovering patterns without predefined hypothesesData exploration involves more complex algorithmsData exploration requires no human intervention

Data exploration and data mining are two different processes involved in understanding complex data sets.

1. Data Exploration: This is the initial step in data analysis, where users make themselves familiar with the data, check for missing data, look for anomalies, or test hypotheses. It involves the use of visual exploration to understand what is in a dataset and the characteristics of the data. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data.

2. Data Mining: On the other hand, data mining is a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Data mining is done through the use of complex algorithms that segment the data, and then check it for patterns. The patterns identified can then be used to further analyse and predict future behaviour.

In summary, data exploration is about discovering patterns with a predefined hypothesis, while data mining is about discovering patterns without predefined hypotheses. Data exploration involves simpler algorithms and requires human intervention, while data mining involves more complex algorithms and can often be automated to a large extent.