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What Exactly Is Data Mining Technique In Data Science?
Data Science is an interdisciplinary field that combines techniques in mathematics, computer programming, statistical and engineering methods to extract insights from large sets of structured and unstructured data. The data analysis process in Data Science is a sequential approach that includes multiple steps like Data Mining, Statistical Computing and Data Modeling & Data Exploration. Data mining is one of the most crucial steps in Data Science that deals with the process of mining data from relevant sources to identify patterns and & to find the relation between different data sets.
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What Is Data Mining?
Data Mining is a crucial step in the data analysis process in Data Science. It deals with the process of Searching, Collecting, Filtering and Analyzing. By the application of Data Mining technique, analysts can find accurate solutions to most of the complex data related problems. The extensive use of Data Mining technique can be seen in the business sector as it helps enterprises in collecting & retrieving information from millions, billions and even trillions of pieces of data. The analyzed information can be used by stakeholders to explore new business ideas & deliver the best customer centric service.
Major Elements In Data Mining:
The major elements in the Data Mining process are
- Data extraction & load operation data onto the data store system
- Data management in multidimensional database system
- Explore the insights from data using Statistical Models & Machine Learning algorithms
- Visually depicting the insights from the data in the form of graph or table
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Kumar Raja is a multidisciplinary writer, and lifelong learner. He’s a Digital Marketer in the making who spends his time analyzing the developments in the tech world. He’s very passionate about helping people understand the latest trends in the tech world through his well-researched articles. He’s able to condense complicated information about the latest technologies into easily digestible articles.