Last Updated on by Kumar Raja

Different Attributes In The Data Transformation Process In Data Science

Data Scientists are must to have professionals inside the workforce of every IT, Software & Business enterprise. With Big Data having become the new oil that fuels the business development process, Data Scientists have become the need of the hour for every organization. Without proper expertise and knowledge of professionals who can handle Big Data, it wouldn’t be possible for any enterprise to explore the benefits of their Big Data reserves to its full potential.

At present, the advancements in the field of Big Data technology are happening at a rapid space & this led to the discovery of new techniques to dig deep inside the data & explore knowledgeable insights from it. Data Transformation is one of the advanced techniques in Data Science that would help the Data Scientists to better handle the Data Mining process. Develop real-world skills in Data Mining & several other prominent techniques in Data Science with our advanced Data Science Training In Hyderabad program.

Now, let’s look at the major attributes in the Data Transformation process in Data Science.

  • Smoothing

Smoothing is a technique that helps the Data Scientists to eliminate noise from the data.

  • Aggregation

Analysts would be performing aggregation operations on Big Data. I.e., the weekly sales data is aggregated to calculate the monthly and yearly total.

  • Generalization

In the process of Generalization, Data Scientists will making use of hierarchies concept to replace low-level data by higher-level data

  • Normalization

Data Scientists perform Normalization technique on Big Data when the attribute data are scaled up or scaled down. Know more in-depth about the Data Transformation & other prominent techniques in Data Science with the help of Kelly Technologies Data Science training program.

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