13 Aug 2020


Data mining is not a new invention that came with the digital era. The concept has been in existence for more than a century, but focused more on the public in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing presented the idea of ​​a universal machine that could perform calculations similar to those of modern computers. Alan Turing developed the Turing Test in 1950 to determine if a computer has real intelligence or not.

To pass his exam, a computer needed to deceive a human by making him believe that he was also human. Just two years later, Arthur Samuel created the Samuel Checkers Game Program, which seems to be the world’s first self-study program. He learned miraculously as he played and improved by winning by studying the best moves. Since then we have come a long way. Companies are now taking advantage of data mining and machine learning to improve everything from their sales processes to the interpretation of finances for investment purpose.

As a result, data scientists have become vital employees in organizations around the world as companies seek to achieve larger goals with data science like never before. With the large amount of data that prevails in the business world, a number of terms of data tend to appear, and many do not understand what they mean. What is data mining? Is there a difference between machine learning and data science? How do they connect with each other? Is machine learning not just artificial intelligence? These are all good questions, and discovering your answers can provide a deeper and more rewarding understanding of the science of data and analysis and how they can benefit a company.

Both the data mining and the machine learning are rooted in information science and can be placed under the same umbrella. They often intersect or confuse each other, but there are some key distinctions between the two.  Some differences between data extraction and machine learning, and how they can be used are given below-

The key difference between machine learning and data mining is the way they are used and applied in our daily lives. For example, data extraction is often used in machine learning to see the connections between relationships. Uber uses machine learning to calculate trips or meal delivery times for Uber EATS. Data mining can be used for a variety of purposes, including financial research. Investors can use data extraction and web tracking to view the finances of a new company and help determine if they want to offer financing.

A company can also use data mining to help collect data on sales trends to better inform everything from marketing to inventory needs, as well as to secure new potential customers. Data mining can be used to analyze profiles of social networks, websites and digital assets to gather information about the ideal prospects of a company to start an outreach campaign. With this amount of information, a data scientist can even predict future trends that will help the company prepare well for what customers may want in the coming months and years. Machine learning incorporates the principles of data mining, but you can also create automatic correlations and learn from them to apply them to new algorithms. It is the technology behind autonomous cars that can quickly adapt to new conditions while driving.

Machine learning also provides instant recommendations when a buyer purchases an Amazon product. These algorithms and analysis are aimed at constantly improving, so that the result can be more precise over time. Of course Machine learning is not used as widely as artificial intelligence, but it always remains an impressive feat to learn something new and improve. Banks are already using and investing in machine learning to help detect fraud when a provider passes credit cards. CitiBank invested in the global data science company Feedzai to identify and eradicate financial fraud in real time through online banking transactions and ATMs.  Thus this technology helps to quickly identify fraud and can help retailers protect their financial activity.