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The Concept Of Data Wrangling In Data Science-Towards Career In Data Science

If you are an analytics career enthusiast or more precisely Data Science then you must surely have heard about the concept of Data Wrangling. This is as unique concept in Data Science which has gained quite a good prominence. However there are quite a lot of people who aren’t aware of this concept. If you really wish to excel in your career in Data Science then, you must be very fluent in handling this technique.

What Is Data Wrangling?

Data Wrangling can be defined as the system process of cleaning, restructuring and enriching the raw data so as to convert it into a more usable format. With Data Wrangling, the process of decision making becomes a lot easier since data is made ready to explore.

This process has gained a lot of fame especially among the top organizations that deal with large volumes of Big Data. Also, with the advent of Data Wrangling the process of analyzing large volumes of Big Data has become a lot easier & quicker.

Sequence Of Steps Involved In Data Wrangling-

The sequence of steps that are involved in the process of Data Wrangling are

  • Discovering

In this first step, Data Scientists will be working towards understanding the data in a precise manner. Having a better idea of the data in hand will help them to know if this data is qualified enough to achieve the desired objectives.

  • Structuring

Most of the data which is collected from various sources remains unorganized & unstructured. In this process data is given a proper structure in a format that best suits the analytical method used. The quality of the output in the process of analysis relies on the effectiveness in which data is structured.

  • Cleaning

For accurate output from the analysis process, data is to be cleaned. Null values will have to be changed, and the formatting will be standardized in order to make the data of higher quality.

  • Enriching

In this process Data Scientists will have to take stock of what is in the data and make it more precise for the process of analysis by adding some additional data to it.

  • Validating

Validation involves a series of steps that helps in determining the consistency, quality and the security of the data.

  • Publishing

The prepared wrangled data is published so that it can be used further down the line

Get to more about such interesting aspects in the Data Science model by being a part of the Analytics Path Data Science Training In Hyderabad program.