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Introduction To Data Mining In Data Science
With data having become the major necessity for the enterprises across all the major platforms, the demand for effective data management & analytics solutions has gone skyrocketing. Data Mining is an advanced technique in Data Science that helps the enterprises in collecting data from multiple sources & which would be then prepared & made ready for analysis.
All the data that is collected cannot be directly analyzed as most of the data remains unstructured & has the presence of anomalies. So, data needs to be organized first which is done by separating the meaning full data from the noisy data. This process of separation is done by a technique called Data Mining.
If you wish to excel in career as a Data Scientist, then mastering the skills in Data Mining is a must. Build real-world expertise in Data Mining by working on real-time projects as a part of the Data Science Training In Hyderabad program by Kelly Technologies.
What is Data Mining?
Data Mining is the technique following analysts can extract enormous insights from enormous levels of Big Data. The process of Data Mining can be considered as the preliminary stage in the data exploratory process in Data Science.
Different Data Mining Techniques in Data Science-
It is obvious that most of the business data is generated from multiple sources rather than one single source. So, analysts or Data Scientists can make use of various Data Mining techniques that are mentioned here below.
- Clustering Analysis
- Sequential Patterns or Pattern Tracking
- Decision Trees
- Outlier Analysis or Anomaly Analysis
- Neural Network
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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.