In data science, what does the acronym “ETL” stand for?

Encode, Tokenize, Leverage
Estimate, Test, Label
Extract, Transform, Load
Explore, Train, Learn

The correct answer is C. Extract, Transform, Load.

ETL is a process that involves extracting data from a source, transforming it into a format that can be used by a target system, and loading it into the target system.

The extract phase involves identifying the data that needs to be extracted from the source system. The transform phase involves cleaning, transforming, and enriching the data so that it can be used by the target system. The load phase involves loading the data into the target system.

ETL is a common process in data warehousing, data integration, and data migration. It is also used in data science to prepare data for analysis.

The other options are incorrect.

Option A: Encode, Tokenize, Leverage. This is not a common acronym in data science.

Option B: Estimate, Test, Label. This is not a common acronym in data science.

Option D: Explore, Train, Learn. This is not a common acronym in data science.