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Answer: Use summary statistics and histograms to identify outliers and missing values.
Profiling data involves using summary statistics to get an overview of the data distribution and histograms to visualize these distributions. This helps in identifying outliers and missing values effectively, which are critical for ensuring data quality.
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You are asked to profile data from a newly acquired dataset in Microsoft Fabric. The dataset includes customer information for a telecom company and has fields such as 'CustomerID', 'Age', 'Gender', 'ServiceType', and 'Churn'. Describe the process you would follow to understand the structure, content, and quality of this data. Include specific techniques you would use to identify outliers, missing values, and potential data inconsistencies.
A
Use summary statistics and histograms to identify outliers and missing values.
B
Apply clustering algorithms to detect data inconsistencies.
C
Perform correlation analysis to understand data structure.
D
Use box plots and scatter plots to identify outliers and missing values.
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