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The Six Stages Of Data Processing
Data Processing can simply be interpreted as the process of collecting data from various sources to extract insights that are encrypted deep inside data. The process of data processing is usually handled by Data Scientists and it depends on the following stages.
Data Collection: Data is collected from multiple sources like data lakes and data warehouses.
Data Preparation: The collected data is then prepared to be free from any form of data anomalies and to reduce data redundancy (incomplete data or incorrect data). This process ensures strict data quality so that accurate interpretations can be made by analyzing it.
Data Input: Once data is cleaned and made free from anomalies a language that can be easily understandable and is converted into a format that can be understood by machines.
Processing: The collected data is then processed by using advanced algorithms in Machine Learning that can help in identifying data patterns and to uncover relations between data patterns.
Data Interpretation: The output that is obtained from the process remains in a format that cannot be understood by people from non analytical background.
Data Storage: At this stage, the processed data gets stored for future use.
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What Is The Need for Data Cleaning?
Data Cleaning is a very crucial process in Data Science that helps Data Scientists in ensuring that the data collected is free from different sources is accurate, and reliable. Data of good quality will help in decreasing the use of sophisticated algorithms for analyzing data.
Data Cleaning can be performed in two different steps
- Data Science would exclude irrelevant & duplicate values from data.
- Errors related to missing values are addressed by using different techniques.
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