Data cleaning, merging, SQL queries, ETL and transformation methods, outliers and data quality issues.
40 cards · basic cards · AI-written, checked twice. Edit anything.
- What is the primary purpose of data cleaning?
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To ensure data is accurate, complete, and consistent for analysis
- Name three common steps in the data cleaning process.
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Removing duplicates, handling missing values, and correcting inconsistent formats
- What does deduplication mean in data cleaning?
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Removing duplicate rows or records that represent the same entity
- What is whitespace trimming used for?
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Removing leading and trailing spaces from text fields
- In data cleaning, what is a NULL value?
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A missing or absent value in a data field
- What are three ways to handle missing values in a dataset?
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Delete rows with missing values, fill with mean/median/mode, or use imputation
- What is data validation in the context of data cleaning?
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Checking that data conforms to required format, type, and value constraints
- What is data transformation?
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Converting data from one format or structure into another suitable for analysis
- What is an outlier in a dataset?
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A data point that is significantly different from other observations
- How can Z-score be used to identify outliers?
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Values with absolute Z-score greater than 3 are typically considered outliers
- What is the Interquartile Range (IQR) method for detecting outliers?
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Points below Q1 minus 1.5 times IQR or above Q3 plus 1.5 times IQR are outliers
- What is data consistency?
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Ensuring that data values are uniform in format and do not conflict across the dataset
- What does data accuracy mean?
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Data correctly represents the true value of what it is measuring
- What is data completeness?
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All required data fields contain values with no missing entries
- What does an INNER JOIN return?
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Only rows where the join condition is true in both tables