DATA SCIENCE VS DATA MINING: EXPLORE THE KEY DIFFERENCES

DATA SCIENCE VS DATA MINING: EXPLORE THE KEY DIFFERENCES 1

In 2015, Facebook faced accusations of violating privacy by handing out information relating to millions of its users to Cambridge Analytica, a political consulting firm. It turned into a major controversy. Consequently, it created much awareness among the masses regarding the significance of big data. And why companies maybe after the personal information of individuals. However, many industry leaders and bigwigs are now already aware of the potential benefits data offers. Like Clive Humby, a British mathematician and entrepreneur are known to quote:

“Data is the new oil.”

And without a doubt, this quote speaks volumes of truth. When we run a company or any organization, we produce a plethora of information. However, this information is not useful until it gets dissected and organized to make valuable conclusions. The resultant of this process of structuring information is known as big data. And the analysis of this end product is known as data science.

When we talk about data science, we often encounter several terms that may overlap each other. They may boggle our minds, and we may be left wondering if they are the same thing or not. However, these terms often have entirely separate meanings, such as data science and data mining.

What Do Both These Terms Mean?

In simple words, data science is the art of capturing data, organizing it, and then analyzing it to form certain conclusions or deductions. Data scientists use various tools such as algorithms, scientific methods, processes, and systems to extract insights from organized or disorganized data. On the other hand, data mining is a technique used in data analysis to discover usable information and further utilize it to unearth patterns. It is a sub-field of computer science and statistics, with the aim being at extracting useful information. Data mining focuses on producing valuable designs in data for further use and analysis.

Here we have a list of the key differences between data science and data mining if you are still confused.

  • The first and foremost difference lies in the definition. Data science is everything that includes storing data, structuring it, analyzing, and producing conclusions out of it. However, data mining is a technique used to discover useful information and extract valuable patterns. Data science is an entire field, whereas data mining is a method/technique for data analysis. And this brings us to the next main variance.
  • Much like the fields, the scope of work varies massively as well. A data scientist can single-handedly perform a variety of roles. They are considered deep learning engineers, artificial intelligence (AI) researchers, machine learning engineers, data analysts, data engineers, etc. They fit into various roles that they can take up after completing a technical learning program in this field. If you wish to get the detailed hang of it, you can search for masters in data science jobs and see what the market offers. The roles are multi-faceted, and so is the field of data science.
  • However, data mining roles are often not this vast. They commonly do not have to perform all these roles to fit into a data mining function at work.
  • Data mining is a subset of data science. Like we discussed earlier, data mining is a method used in data analysis. If we talk about data science, it is a massive field that stretches far and wide. It includes statistics, data visualizations, social sciences, data mining, etc. And therefore, data mining is a sub-field of data science and not an entirely separate subject.
  • Data science came around the 1960s, which makes it quite an established subject of study. On the other hand, the term data mining became famous in the 1990s.
  • Likewise, data scientists and data miners also vary based on various kinds of data. Data scientists are well-equipped to work with all sorts of information, be it structured, semi-structured, or unstructured. However, data mining professionals commonly work with information that has been structured and organized.
  • If we look into the kind of work both types of professionals perform, we will find another difference. Data mining professionals focus mainly on discovering and analyzing patterns in a given set of data. Data scientists also do this; however, their role does not only revolve around this. Data scientists study information and, based on historical and present information, forecast or predict future
  • Data mining majorly focuses on business processes, whereas data scientists focus on the study of data.

Conclusion:

Data science is a broad field of study. Data science professionals can work as a business analyst, data engineer, data and analytics manager, data architect, etc. The roles are diverse and vast. However, data mining professionals most commonly work as data analysts. Their sole responsibility revolves around dissecting data and discovering useful information that can prove valuable in a business or organization setting. If you are looking to choose one as your field of study but are perplexed about the different terms, do not fret. The pointers mentioned above are the core differences between a data scientist and a data miner. You can pick and choose which field interests you more and go along with it!

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