What is the Data Preparation Process in Data Science?

Posted by sairaj tamse on July 20th, 2022

Adequate data preparation facilitates analysis, reduces mistakes and inaccuracies that may arise during processing, and increases users' access to all processed data. New tools allowing users to independently cleanse and certify data have also made it simpler.

What is Data Preparation?

Data preparation is the cleaning and converting of raw data before processing and analyzing it. Before processing, it is a crucial phase that frequently entails data reformatting, data corrections, and mixing data sources to enrich data.

Data preparation is frequently time-consuming for data experts or business users. Still, it is necessary to place data in context to generate insights and remove bias from bad data.

For instance, the standardization of data formats, enrichment of the source data, and/or removal of outliers are typically part of the data preparation process. For more in-depth knowledge on data preparation technique, visit Learnbay’s data science course

Benefits of Data preparation 

According to 76 percent of data scientists, data preparation is the most challenging aspect of their work, but clean data is required for making effective, precise business decisions. 

Advantages of data preparation are as follows:

  • Fast mistake correction:

Before processing, data preparation helps detect errors. These inaccuracies are harder to comprehend and fix once data has been removed from its original source.

  • Produce superior data:

Datasets are cleaned and reorganized to ensure they are of the highest quality for analysis.

  • Make wiser business choices:

Better data of a higher quality than can be processed and analyzed more rapidly and effectively results in business decisions that are more timely, effective, and of a higher caliber.

Additionally, data preparation transfers to the cloud along with data and data processes for even more significant advantages, such as:

  • More scalability:

Data preparation on the cloud can develop at the speed of business. Enterprises don't need to be concerned with the underlying infrastructure or try to predict how it will change.

  • Future-ready:

To enable new features or bug fixes as soon as they are made available, cloud data preparation automatically upgrades. As a result, businesses may stay ahead of the innovation curve without suffering delays or additional expenses.

  • Collaboration and data use are being accelerated:

Data preparation is always active in the cloud, necessitates no technical setup, and enables team collaboration for quicker outcomes.

 Data Preparation Steps

Industry, organization, and necessity all affect the intricacies of the data preparation process, but the overall framework is relatively constant.

  1. Gather data: 

Finding the appropriate data is the first step in the data preparation process. This can be added ad hoc or from an existing data catalog.

  1. Locate and evaluate data: 

Discovering each dataset is crucial after data collection. This step aims to understand the data and what has to be done before the data is relevant in a given context.

Although discovering new data is challenging, Talend's data preparation platform provides visualization capabilities that enable customers to profile and browse their data.

  1. Validate and clean up the data: 

The most time-consuming step in the data preparation process, cleaning up the data, is essential for eliminating inaccurate data and filling in any gaps. Here, crucial duties include:

  • Removing auxiliary information and outliers.

  • Completing blank values.

  • Adjusting data to a predetermined pattern.

  • Masking items for sensitive or confidential info.

Data must be validated after cleaning by checking for errors made during the first data preparation stage. It is common for a system error to be discovered during this stage and to need to be fixed before continuing.

  1. Enhance and transform data:

Data transformation involves changing the format or value entries to achieve a specific result or to make the data more understandable to a broader audience. Data must be enhanced by connecting to and adding to other relevant information to acquire more profound insights.

  1. Keep data:

Once it has been prepared, the data can be saved or fed into a third-party program, like a business intelligence tool, opening the door for processing and analysis.



Summary:

Overall, eliminating mistakes and normalizing unprocessed data prior to processing generates higher quality data for analysis and other data management-related activities. It is crucial but requires a great deal of time and may necessitate specialized abilities.

However, with a smart data preparation tool, the procedure has become more efficient and accessible to a larger range of people.


To know more about data preparation and other data science techniques, check out a data science course in Bangalore, offered by Learnbay. Get ready to work on various data science projects and earn certification from the prestigious IBM.

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sairaj tamse

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sairaj tamse
Joined: July 7th, 2022
Articles Posted: 27

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