What is the difference between data mining and data analytics?

Data mining is the process of discovering patterns in large data sets, while data analytics is the science of analysing raw data to make conclusions.

Data mining and data analytics are two terms that are often used interchangeably, but they have distinct differences. Data mining is a process that involves the use of methodologies to discover patterns in large data sets. These methodologies include machine learning, statistics, and database systems. The goal of data mining is to extract information from a data set and transform it into an understandable structure for further use. It is a crucial part of knowledge discovery in databases (KDD), which is the overall process of converting raw data into useful information.

On the other hand, data analytics is the science of analysing raw data in order to make conclusions about that information. It involves applying an algorithmic or mechanical process to derive insights and includes several techniques and processes automated into mechanical processes and algorithms. Data analytics can be broken down into several types, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

While both data mining and data analytics involve dealing with large amounts of data, their objectives and processes are different. Data mining is more focused on discovering patterns and relationships in data, while data analytics is more focused on drawing conclusions and making predictions based on data.

In terms of application, data mining is often used in fields such as marketing, fraud detection, and healthcare to identify patterns and relationships that can lead to actionable insights. Data analytics, on the other hand, is used in decision-making processes in various industries, including finance, healthcare, and e-commerce, to make data-driven decisions.

In summary, while both data mining and data analytics are important in the field of data science, they serve different purposes and involve different processes. Understanding the difference between the two is crucial for anyone studying or working in the field of data science.