Data Cleaning

Question 1

Dirty Data

Answer 1

Data that’s incomplete, incorrect, or irrelevant to the problem you’re trying to solve.

Question 2

Clean Data

Answer 2

Data that’s complete, correct, and relevant to the problem you’re trying to solve.

Question 3

Data Engineers

Answer 3

Transform data into a useful format for analysis and give it a reliable infrastructure.

Question 4

Data Warehousing Specialists

Answer 4

Develop processes and procedures to effectively store and organise data.

Question 5

Null

Answer 5

An indication that a value does not exist in a dataset. A null is not the same as a zero.

Question 6

Field

Answer 6

A single piece of information from a row or a column of a spreadsheet.

Question 7

Field Length

Answer 7

A tool for determining how many characters can be keyed into a field.

Question 8

Data Validation

Answer 8

This is a tool for checking the accuracy and quality of data before adding or importing it.

Question 9

Data-cleaning tools and techniques

Answer 9

• Remove duplicates
• Remove irrelevant data
• Remove extra spaces and blanks
• Fix misspellings
• Fix inconsistent capitalisation
• Fix incorrect punctuation and other types
• Removing formatting [most Spreadsheet apps have a ‘clear format’ tool in the toolbar.

Question 10

Data merging

Answer 10

The process of combining two or more datasets into a single dataset.

Question 11

Compatibility

Answer 11

How well two or more datasets are able to work together.

Question 12

Compatibility Questions

Answer 12

• Do I have all the data I need?
• Does the data I need exist within these datasets?
• Do the datasets need to be cleaned or are they ready for me to use?
• Are the datasets cleaned to the same standard?