7 AWESOME DIFFERENCE BETWEEN DATA SCIENCE VS DATA MINING

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.

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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.

Kerry Parker

  1. Probably the biggest difference between data science and data mining lies in their terms. Data science is a broad field that includes the processes of capturing of data, analyzing, and deriving insights from it. On the other hand, data mining is mainly about finding useful information in a dataset and utilizing that information to uncover hidden patterns.
  2. Another major difference between data science and data mining is that the former is a multidisciplinary field that consists of statistics, social sciences, data visualizations, natural language processing, data mining etc while the latter is a subset of the former.
  3. The role of a data science professional can be considered as a combination of an AI researcher, a deep learning engineer, a machine learning engineer, or a data analyst, to some extent. The person might be able to perform the role of a data engineer as well. On the contrary, a data mining professional doesn’t necessarily have to be able to perform all these roles.
  4. Another notable difference between data science and data mining lies in the type of data used by these professionals. Usually, data science deals with every type of data whether structured, semi-structured, or unstructured. On the other hand, data mining mostly deals with structured data.
  5. If you consider the nature of work in the fields, you’d be able to find another difference. In data science, you’re not only finding patterns and analyzing them which are key components of the data mining Instead, with the help of data science tools and technologies, you’re expected to be able to forecast future events by leveraging the present and historical data.
  6. The term data science has been around for a long time — since the 1960s whereas data mining became known in the 1990s, amongst the database communities.
  7. While the field of data science concentrates on the science of data, the field of data mining is mainly concerned with the process.

Final Takeaway

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hen it comes to handling the steadily increasing amount of data, both data science and data mining play crucial roles in helping businesses in identifying opportunities and making effective decisions. So, while the objective of both these fields remain similar — to derive insights that can help a business to grow — the key differences lie in the tools and technologies used, nature of work, and in the steps to perform respective responsibilities to attain that objective.

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