Data Cleaning

These are reasons why data are dirty, except ______

• incomplete

• outdated

• garbage in garbage out

• inconsistent

Cleaning data involved all these, except ______

• Handle missing data

• Rename columns

• Convert data type

• Improper encoding

Your data is considered ___ when you did not remove any data from the data set.

• clean

• raw

• untouchable

• original

____ data are the data that is ready for analysis.

• Cooked

• Processed

• Formatted

• Clean

Dates normally has ______ issues.

• naming convention

• different representations

• formatting

• redundancy

Data is missing or not referenced violates which data quality dimensions?

• conformity

• accuracy

• consistency

• integrity

Data is stored in a non-standard format violates which data quality dimensions?

• conformity

• completeness

• accuracy

• consistency

Geolocation variables include all these, except _______

• Zipcode

• Property tax amount

• Longitude

• Latitude

Data preparation or pre-processing involved these tasks.

• data cleansing, reshaping, conversion, encoding

• data cleaning, integration, transformation, reduction

• data cleaning, munging, wrangling, formatting

• none of the mentioned

Data scientists spend most of their time doing ______

• finding and getting data

• thinking about problems and questions to ask

• preparing and cleaning data

• interpreting analysis results