As organizations and businesses have started to realize that there’s a huge value hiding in the massive amount of data they capture on a regular basis, they’ve been trying to employ different techniques to realize that value. While the ultimate goal is to produce actionable insights from that data, the tech world is getting filled with a significant number of technical terms. And among all these terms, probably the most talked-about terms are data science and data mining. Though some people use them interchangeably, they come with significant differences. Here’re seven most prominent differences between data science and data mining.
Data mining refers to the process where a large amount of data is analyzed to extract new and hidden information from it, which can then be used to boost business efficiency. In other words, you can say data mining searches for valid, hidden, and potentially useful patterns in large data sets. Since data mining needs multi-disciplinary skills, you’ll have to use statistics, machine learning, AI (artificial intelligence), and database technology. Since data mining helps you discover previously unknown/unsuspected relationships amongst the data, you can use the insights gathered from it for scientific discovery, sales and marketing, fraud detection, etc.