Why is data cleaning crucial in data science?
An example of an AI chatbot conversation about the importance of data cleaning in data science.
Why is data cleaning crucial in data science?
Data cleaning is essential because it ensures the accuracy and quality of data, which directly impacts analysis and decision-making.
What are some common data cleaning techniques?
Common techniques include removing duplicates, handling missing values, correcting inconsistencies, and standardizing formats.
How does poor data quality affect data science projects?
Poor data quality can lead to incorrect insights, flawed models, and ultimately, misguided business decisions.
Can you give an example of a data cleaning process?
Sure! A typical process might involve identifying missing values, filling them in with averages, and removing outliers.
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