Data Science Vs Data Mining | A Detailed Comparison

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Organizations and corporations have been attempting to use a variety of approaches to discover the enormous potential that is hidden in the immense quantity of data that they regularly collect. The IT sector is becoming increasingly populated with information and technical words, even if the end purpose is to get useful insights from that data.

Difference Between Data Science Vs Data Mining

And out of all of these phrases, data science, and data mining are perhaps the two that get the most attention. Despite the fact that some individuals use them identically, they have distinct characteristics.

Before moving on to the potential differences, let’s get the terminologies straightened.


What is Data Science?


Data science is an interdisciplinary topic or domain where information and insights are extracted from enormous amounts of organized and unstructured data using analytical techniques, algorithms, processes, and systems. This is then utilized to create analytical models that are predictive, prescriptive, and prescriptive.

Deep learning, big data, and data mining are all connected to data science. It involves mining, capturing (for the purpose of creating the model), analyzing (for the purpose of verifying the model), and using the data. Business, computer science, and statistics are all incorporated into data science. The Data Science course duration is not strictly specified. It usually takes six to twelve month-long training that is designed to provide applicants with a solid foundation in the discipline.

Data science is used in a variety of applications, such as,

  • Detection of risk and fraud
  • Personalized advertisement
  • Speech synthesis
  • Tips for healthcare websites
  • Contemporary image recognition
  • Internet lookup
  • Arranging an airline route

What is Data Mining?


Finding patterns in huge datasets is a technique known as data mining. It uses techniques that combine database management, analytics, and machine learning.

Using advanced mathematical methods, this multidisciplinary subject of statistics and machine learning aims to extract information from sizable datasets or libraries of material data and turn it into an understandable structure for subsequent use.

By carefully extracting, evaluating, and processing raw data to find patterns and connections that might be useful for enterprises, data mining helps to generate insights.

With the use of basic or sophisticated software, data mining—also known as knowledge discovery in data (KDD)—can be carried out. Data mining has several uses, including the following:

  • Market research
  • Examination of finances
  • Higher learning
  • Detection of fraud

Various services are used in data mining operations, including:

  • Web analysis
  • Mining text
  • Mining audio
  • Mining videos
  • Mining social network data
  • Data mining using images

Let’s take a look at the terminology development before moving on to the technical differences. The present usage of the phrases will be made clear by a historical origin.

  • The term “Data Science” dates back to the 1960s, however at that time, “Computer Science” was preferred. Right now, it means something very different.
  • D. J. Patil and Jeff Hammerbacher were the first people to refer to themselves as “data scientists” while describing their positions at LinkedIn and Facebook, respectively, in 2008.
  • The “Sexiest Job of the 21st Century” according to a 2012 Harvard Business Review article was “Data Scientist.”
  • Parallel to this, the term “data mining” has emerged. In the 1990s, it spread throughout the database community.
  • KDD is where data mining got its start. KDD (Knowledge Discovery in Databases) is a method for extracting knowledge from data in databases. And a significant KDD subprocess is data mining.
  • KDD and data mining are frequently used interchangeably. Despite the fact that these names entered the scene on their own, they frequently work best together because they are all strongly tied to data analysis.

Differences Between Data Science and Data Mining


  • The words used to describe data science and data mining are where the largest differences reside. Data mining focuses on identifying meaningful data in a dataset and using it to detect hidden patterns, but data science is a broad discipline that entails gathering data, analyzing it, and drawing meaningful insights from it.
  • Data mining is a subset of data science, which is a multidisciplinary area that includes statistics, data visualizations, sociology, natural language processing (NLP), and data mining. This is another significant distinction between the two fields.
  • A data scientist is essentially a mix of a researcher in artificial intelligence (AI), a machine learning engineer, a deep learning engineer, and a data analyst. However, a data scientist can also play some of these functions, therefore a data mining expert may not be able to do them all.
  • The kind of data utilized is another obvious distinction. Structured, unstructured, and semi-structured data are all subjects of data science. Data mining, however, mostly works with organized data.
  • There is still another distinction between data science and data mining when it comes to the nature of the activity. A crucial part of data mining is finding patterns and evaluating them. The same is true for data science, but it also entails predicting future occurrences by utilizing past and current data with a variety of techniques and technology.
  • Data mining is primarily concerned with the management of technical and behavioral inconsistencies and making predictions, whereas data science emphasizes the science of data.
  • Data science and data mining both play a critical role in assisting organizations in identifying possibilities and making wise decisions with regard to managing the continually rising number of data. While boot camps are worth the investment, the data science Bootcamp fee won’t be hard to afford. There are online boot camps that are provided for free.

Conclusion


You should keep in mind that neither data science nor data mining has official, exact definitions. What constitutes an appropriate definition is still up for discussion in both academics and business. 

However, everyone agrees on the fundamental distinctions and definitions of the two words, which we examined in this essay. Data science and data mining both play critical roles in assisting organizations in recognizing possibilities and making wise judgments whenever it comes to managing the continually growing volume of data.


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