1. Data Cleansing: This is the first step in validating the accuracy and completeness of data. It involves checking for and correcting errors, inconsistencies, and inaccuracies in datasets. This process may include tasks such as removing duplicates, correcting spelling errors, and dealing with missing values.

2. Data Verification: This is a process that ensures the data collected matches the original source. It involves cross-checking the data with other sources to confirm its accuracy. For example, if you have collected data from a survey, you might verify a sample of responses directly with the respondents to ensure the data is accurate.

3. Data Validation: This is a process that checks if the data meets certain criteria or rules. For example, if a field in a dataset is supposed to contain dates, a data validation check might involve confirming that all the values in that field are valid dates. This process helps to ensure the data is complete and suitable for analysis.

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1. Data Cleansing: This is the first step in validating the accuracy and completeness of data. It involves checking for and correcting errors, inconsistencies, and inaccuracies in datasets. This process may include tasks such as removing duplicates, correcting spelling errors, and dealing with missing values.

2. Data Verification: This is a process that ensures the data collected matches the original source. It involves cross-checking the data with other sources to confirm its accuracy. For example, if you have collected data from a survey, you might verify a sample of responses directly with the respondents to ensure the data is accurate.

3. Data Validation: This is a process that checks if the data meets certain criteria or rules. For example, if a field in a dataset is supposed to contain dates, a data validation check might involve confirming that all the values in that field are valid dates. This process helps to ensure the data is complete and suitable for analysis.

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1. Data Cleansing: This is the first step in validating the accuracy and completeness of data. It involves checking for and correcting errors, inconsistencies, and inaccuracies in datasets. This process may include tasks such as removing duplicates, correcting spelling errors, and dealing with missing values.

2. Data Verification: This is a process that ensures the data collected matches the original source. It involves cross-checking the data with other sources to confirm its accuracy. For example, if you have collected data from a survey, you might verify a sample of responses directly with the respondents to ensure the data is accurate.

3. Data Validation: This is a process that checks if the data meets certain criteria or rules. For example, if a field in a dataset is supposed to contain dates, a data validation check might involve confirming that all the values in that field are valid dates. This process helps to ensure the data is complete and suitable for analysis.

Describe three techniques used to validate the accuracy and completeness of data before analysis

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