Question

Why are pre-cleaning steps important to complete before data cleaning? a) To increase data security b) To speed up the data cleaning process c) To identify outliers and anomalies d) To remove all data from the dataset

Answer 1

c) To identify outliers and anomalies

Step-by-step explanation:

Pre-cleaning steps are important to complete before data cleaning primarily to identify outliers and anomalies. Data pre-cleaning involves initial data assessment and data profiling to understand the quality and characteristics of the dataset. This step helps in identifying and documenting any data anomalies, such as missing values, incorrect data types, outliers, or inconsistencies.

Identifying outliers and anomalies is crucial because they can significantly impact the results of data analysis or machine learning models. Removing or handling outliers appropriately is often necessary to ensure that the data cleaning process is effective and that the final dataset is suitable for analysis or modeling.