Last Updated on by Kumar Raja

Different Data Modelling Concepts In Data Science

The prominence of Big Data in the data modelling process in Data Science is well known to everyone. Big Data is like new oil that empowers the decision making process where as Data Science is the tool that helps in extracting a value from Big Data.  Both Data & Data Science are very crucial in the enterprises decision making process & they also help to forecast the demand & predict the trends.

Data Modelling is s crucial step in the Data Science process that helps in making most of out the Big Data. If you are intended to predict something from the data or need make decision from the data then you must surely build a data model & train it with the help of advanced algorithms in Machine Learning to enhance the efficiency of the system.

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Now, let’s know in-depth about different algorithms that are used in the Data Modelling process.

  • Dimensionality Reduction

This is an unsupervised algorithm model that assists the Data Scientists in implementing a fixed set of raw data into a well defined structured format. As we know how random variables can affect the efficiency of the system, Dimensionality Reduction algorithm helps in reducing the random variables. This algorithm also assists the Data Scientists in the data visualization process.

  • Clustering

Clustering algorithms are very crucial when it comes to classification of variables of similar types into individual groups. Just like Dimensionality Reduction, Clustering algorithms also belong to the category of unsupervised algorithms.

  • Linear Regression & Classification

If the data analysis process involves intense statistical modelling then relying on Liner Regression algorithms would be the ideal choice. The use of Classification algorithms is very crucial for sentimental analysis.

Know more in-depth about the Data Modelling technique in Data Science with the help of Kelly Technologies Data Science training program.

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