Cleaning your data is an essential step in the data analysis process. Verifying and reporting your cleaning is a way to show that your data is ready for the next step. In this part of the course, you'll find out the processes involved with verifying and reporting data cleaning as well as their benefits.

• Describe the process involved in verifying the results of cleaning data
• Describe what is involved in manually cleaning data
• Discuss the elements and importance of data-cleaning reports
• Describe the benefits of documenting the data cleaning process

Manually cleaning data

Verification

A process to confirm that a data-cleaning effort was well-executed and the resulting data is accurate and reliable

The verification process confirms that data cleaning was well executed and the resulting data is accurate and reliable. To verify data, analysts recheck the data-cleaning effort, manually fix errors, and consider whether the data is credible and appropriate for the project.

How to show your change?

Changelog: A file containing a chronologically ordered list of modifications made to a project

See the big picture when verifying data-cleaning

1. Consider the business problem
2. Consider the goal
3. Consider the data

Correct the most common problems

Make sure you identified the most common problems and corrected them, including:

• Sources of errors: Did you use the right tools and functions to find the source of the errors in your dataset?
• Null data: Did you search for NULLs using conditional formatting and filters?
• Misspelled words: Did you locate all misspellings?