Data _______ involves correcting wrong or inconsistent parts of the data.
- Augmentation
- Cleansing
- Transformation
- Validation
Data cleansing is the process of identifying and correcting errors or inconsistencies in the dataset. It ensures that the data is accurate and reliable for analysis. Data augmentation, validation, and transformation are different aspects of data preprocessing.
Loading...
Related Quiz
- For selecting a column in a DataFrame in dplyr, which function would you typically use?
- What is the purpose of the lapply() function in R?
- The Pandas function _______ is essential for reshaping data from wide format to long format.
- How does an ETL tool typically handle data from different sources with varying formats?
- For real-time stream processing in Big Data, _______ can be used to build complex transformation pipelines.