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The Concept Of Data Mining Explained

Data Science is a leading analytics technology which is precisely being for the process of Big Data & its analytics management. It is quite effective in tackling challenges ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and vis­ualization towards successfully solving a Data Science problem.

Data Mining is one among the crucial steps involved in the process of solving a Data Science problem & so Data Mining is a must to have skill in the arsenal of a Data Scientist. In this blog post let’s help you towards understanding the concept of Data Mining.

Defining Data Mining-

As the process of Data Science is all about exploring & analyzing the data so as to uncover hidden patterns & insights, it is very much important to have the presence of accurate data that can help in solving the problem.  This is where data mining comes into the play. The whole concept of Data Mining is to enhance business decision making process by predicting future outcomes which can ultimately cut costs and increase revenue for the organizations.

Explaining The Process Of Data Mining:

The process of Data Mining is carried out in six different steps

  • Business Understanding

The very first step in the process of Data Mining is to understand the goals of the project & then determining how data mining can help you reach that goal. Timelines, actions, and roles are assignments in this step.

  • Data Understanding

Once the objectives are defined the next step involves collecting the data from all the applicable data sources. Also, In order to explore the properties of the data, analysts make use of various data visualization tools.

  • Data Preparation

In this process data is cleansed, and missing data is included to ensure it is ready to be mined. Sometimes this can be a time taking process if data has to be collected from various sources. To ensure quick flow of the process Data Scientists make use of modern database management systems instead of using one single system.

  • Data Modeling

Mathematical models are then used to find patterns in the data using sophisticated data tools.

  • Evaluation

The insights that are extracted are then analyzed to determine if they should be deployed across the organization.

  • Deployment

In the final stage, the data mining findings are shared across everyday business operations. Get to know more about the data modelling process in Data Science domain by being a part of the Kelly Technologies Data Science Training In Hyderabad program.

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