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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
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Data formats have expanded along with the volume of data that is being produced. The unstructured data that has been collected from certain corporate enterprises comes in a variety of formats. If we believe it to be beneficial, it will be essential to alter the format of this data in accordance with the circumstances and requirements of the data. When it comes to feeding and training algorithms with data, data science algorithms have distinct demands and expectations. In these circumstances, the appropriate data must be pre-processed. Different processes are involved in data preparation. One of the crucial data preparation stages in this context is normalisation.
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The raw data always comes in inconsistent formats. It becomes difficult for the data scientists to convert this inconsistent form of data into a consistent format to make it useful for different perspectives. The data is of no use if you cannot use it for getting different types of benefits from it. Getting some useful insights from the data is the main purpose of applying different data analytics to the specific data set. But before applying some data science algorithms, you need to prepare the data set well.
The process of transforming the data set formats to other necessary formats and forms is known as data transformation. The values of the data set are also modified throughout this procedure in accordance with the scenario's needs and data science methods. Data transformation is carried out during the data preparation step of creating data science models for projects. ETL (extract, transform, and load) is the step of data transformation that is most frequently employed in all data science models.
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When dealing with data saved on cloud devices, the data transformation is carried out in the middle of the process. Nowadays, the majority of data scientists employ the ELT format, in which cloud-based data is initially put into data warehouse software before being subjected to various transformation techniques and procedures. However, when working directly with the warehouse software, data scientists convert the data using the already mentioned ELT format.
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While transforming various forms of data, data scientists must overcome a variety of difficulties. Here are a few difficulties that data scientists encounter most frequently while transforming data.
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The various advantages of the transformation process have all been thoroughly covered. The difficulties experienced by data scientists when implementing data science and transformation tasks have also been covered by our specialists. You must follow our blog if you want to read more articles on data science.
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