The process of data cleansing may involve the removal of typographical errors, data validation, and data enhancement. It generally helps to improve data quality, and the process can be automated or done manually. Also known as data cleansing, it entails identifying incorrect, irrelevant, incomplete, and the “dirty” parts of a dataset and then replacing or cleaning the dirty parts of the data.Īlthough sometimes thought of as boring, data cleansing is very valuable in improving the efficiency of the result of data analysis. What is Data Cleaning?ĭata cleaning is the process of modifying data to ensure that it is free of irrelevances and incorrect information. This article will cover what data cleaning entails, including the steps involved and how it is used in carrying out research. There are so many processes involved in data cleaning, which makes it ready for analysis once they are completed. It can be carried out manually using data wrangling tools or can be automated by running the data through a computer program. It is a very important step in ensuring that the dataset is free of inaccurate or corrupt information. Data cleaning is one of the important processes involved in data analysis, with it being the first step after data collection.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |