Data Science and Data Mining are two sides of the same coin. Having said that, they both have often gone hand in hand in terms of data. Often considered in similar terms with Data mining, Data Science is noticeably a different matter. It can be defined as an interdisciplinary field that with the aid of various processes, algorithms, systems as well as scientific methods to bring forth insights from the collection of data. This can be done in an assortment of forms such as structured accompanied by unstructured. Topics such as Data Mining, Big Data Analytics, data Visualization, Predictive Modeling, Statistics and Mathematics fall under Data Science. Data mining also goes by the name of Data-driven science.

Contrary to Data Science, data mining is a process. As its name suggests, it uses parts of Traditional statistics, artificial intelligence, etc. This process can be defined as the identification of unique patterns from large sets of data by data analysis. Data mining is a subset of data science. It is an assembly of various strategies for the recognition of patterns, trends, or associations. Data mining fails to answer certain specific questions.

The other names that data mining goes by are Information Harvesting, Knowledge Extraction, Information Discovery, and Data Archaeology. And while they both have their own differences, they also contain certain similarities.

While Data Mining is considered to be a process, Data Science as a whole is an area of study.

Approach

The process of data mining deals with business related situations, whereas its counterpart – Data science deals with scientific study.

Aim

The aim of data mining is concerned with duties related to making the collected data more stable. Data Science, on the other hand, aids in the development of products centered around data for an organization / industry.

Purpose

The purpose of data mining is to find trends that are unfamiliar or previously unknown to us. The area of data science aims for the purpose of building predictive models, unearthing unfamiliar facts that were previously unknown, and for the sake of social analysis, etc.

Types of data

Data mining deals with structured data. Whereas Data Science deals with all types of data ranging from the structured data, to the semi-structured, and even unstructured data.

Area of expanse

As mining activities are a part of the Data Science sector, data mining can be called upon as a subset or a constituent of the larger set that is Data Science. The area of data science is composed of several multidisciplinary, such as Data mining, Social sciences, Natural language processing, data visualizations as well as computational social sciences.

Occupational outlook

With the understanding of statistical concepts and know how to navigate across data qualifies for understanding the process of Data Mining. For understanding data science, one must understand concepts such as Operations research, Machine learning, infographic methods as well as programming along with knowledge of the domain.

Resource Box

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