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Sollers is a graduate school located in New Jersey, specializing in clinical research, drug safety and pharmacovigilance training.

Our graduate certificate and masters programs cover a wide range of subjects tailored to this fast growing industry, and our graduates go on to highly successful careers in the pharmaceuticals industry and healthcare industries.

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How is Data Cleaning Ensured for Effective Data Analysis?

Posted by Doctor Dan on Aug 3, 2016 9:12:01 AM

Data cleaning is an important part of data analysis. This is done to eliminate, modify, or restore data depending on its state. Data that is corrupt or redundant apart from duplicate files is removed. Inaccurate data is identified and sorted. Incomplete data is marked and modified. Back up of data is taken before cleaning it to prevent loss of information.

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Here is how data cleaning is done:

Structuring of Heterogeneous Data

  • Errors are detected removed through analytic tools. Curating data is important for anything that goes on the web or in the internal process of a project. Cleaned data is extracted through preprocessing.
  • Variables are set to filter data that is to be analyzed. Decision making is based on the data presented. This is the reason why inconsistencies are removed. Algorithms and many other manual resources are used to clean and sort data before visually presenting it.
  • It also includes correcting values of variables and other entities in the code. To regulate this process effectively, different types of constraints are used. Uniformity and accuracy is ensured when analyzing data.

Verification and Transformation

  • Data that is analyzed is also verified. This is to ensure that the final output of data analysis is effective. The verification process happens multiple times to refine the output.
  • Data is then transformed into a human readable format from a system readable format. The original sources of data are also replaced with the transformed data.
  • Data cleaning is vital for every business. Bad data can affect the decisions of businesses and cost time, resources, and money.

Conclusion

Anomalies are removed and sequences of operations are constructed. Parsing and other statistical methods are used to cleanse the data before execution. Complex data is sorted and presented visually so businesses can take correct decisions that prove lucrative in the long run.   Get more details on Data Science and Data Analysis here

Topics: Data Analysis, Data Science