Difference between Data Mining and Data Science


Companies have recognized the paramount value of data and the insights it provides and this is a reason why many businesses have developed different data analysis and processing tools and techniques.

As a lot of companies are paying more attention to getting actionable insights, the site becomes crowded by many technical terminologies, one of which is data science and data mining. The subsequent article will explicate the essential view of data science and data mining and thus explain their differences.

Data Science:

The phrase “data science” was suggested by Naur in 1974 as a substitute term for computer science. At first, it was merely called data analytics. However, Chikio Hyashi, after some years, helped invent the field of data science which is a merger of data design, collection and analysis. The data science that has thrived recently is closely linked to the emergence of big data at the beginning of the 21st century. This consequential growth in the data volume has compelled the need for extracting insights. This is how data science has become an independent discipline.

Data Science in its interdisciplinary nature creates its powerful effect through the combination of various computer tools, algorithms and machine learning principles to analyze both structured and unstructured data to extract useful information. It is strongly grounded in the theory of statistics, data analytics and modeling, which are used to cope with complex data.

Data scientists are of the highest importance in collecting, analyzing, and combining data which is then used in the decision-making process in the business environment.

Data Mining:

Data mining involves identifying valuable patterns, trends, and insights from unorganized data repositories. It refers to the process of dividing data and predicting probable future events by using probability. Retail companies and financial organizations often use data mining technologies to discover patterns, expand customer base and predict stock prices to be able to react to them and consumer demand.

Data mining provides a multi-dimensional approach to organizations as it allows the prediction of consumer behavior and fighting fraud and spam. Specialized software or algorithms are capable of identifying relationships and patterns hidden in data therefore supplying insights to stakeholders for decision making.

Data Mining vs. Data Science:

Data science and data mining might be similar, but they have some differences and they have been used in various ways in data analytics. Here we discuss about difference between these disciplines.

  • Scope and Definition:

Data science is a rather wide term that covers a wide range of activities, such as data acquisition, analysis and the creation of valuable insights for actions. However, in contrast to data mining which mainly focuses on extracting useful information from datasets and discovering hidden patterns, data analytics involves the processing of data and the identification of insights that are possible to act upon.

  • Multidisciplinary Nature:

Data science represents a multi-disciplinary approach that involves the mixture of the principles of statistics, data visualization, social sciences, NLP (Natural Language Processing) and data mining itself. Consequently, data mining turns our attention to the wider context of data science.

  • Role and Skill Set:

The role of a data scientist is multifaceted, encompassing aspects of “AI scientist”, “machine learning engineer”, “deep learning engineer” and “data analyst”. On the other hand data mining professionals may not necessarily include all the mixed capabilities that are needed for these tasks.

  • Type of Data Utilized:

Data science integrates “structured” and “unstructured data”, including “text”, “images”, “audio” and “video”. There is a significant discrepancy between data mining focusing on structured data and data mining in another area.

  • Nature of Work:

Data science is an area which has a far more comprehensive view of data than data mining in terms of not only finding patterns in it but also predicting future events based on present and historical data using various tools and technologies.

  • Focus and Objectives:

Data science as a science puts data under the spotlight, and its tasks entail problems like anomaly, inconsistency and conclusion prediction. While data mining is mostly focused on the discovery of patterns and the retrieval of significant information from datasets, data science has a broader scope, which involves not only data analysis but also the implementation of new algorithms.

  • Salary Considerations:

Statistics that are associated with data science pay higher compared to data mining, because of the wider knowledge set it has and the more complex and strategic roles it plays. On the other hand, salary levels may vary depending on what specific job role or industry he or she is working in, how much experience they have and where the individual is located.


Both data science and data mining are very similar and useful as they both efforts to bring out/expose the hidden information but they differ in their methodologies, scope and application. A more complex competence in the mentioned divisions allows us to take full advantage of the data analytics potential and to use it effectively in the business framework.

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