Data Transformation

What is data structuring?

• The process of analyzing data for quality

• The process of cleaning data

• The process of changing the organization and relationships among data fields

• The process of merging data from different sources

What does data pivoting involve?

• Separating data into multiple fields

• Combining different data sources

• Rotating data from rows to columns

• Summarizing data at different levels

What is the purpose of data standardization?

• To remove duplicate records

• To ensure data is in a common format for analysis

• To visualize data trends

• To filter out irrelevant data

What is data cleaning?

• The process of analyzing data for errors

• The process of updating data to be consistent, accurate, and complete

• The process of aggregating data

• The process of merging data from different sources

What are cryptic data values?

• Data values that are duplicated

• Data values that are consistently formatted

• Data items that have no meaning without understanding a coding scheme

• Data values that are easily understood

What is data de-duplication?

• The process of standardizing data formats

• The process of filtering out irrelevant data

• The process of removing identical records from a dataset

• The process of combining data from multiple sources

What is data validation?

• The process of cleaning data

• The process of analyzing data to ensure it has high-quality properties

• The process of structuring data for analysis

• The process of merging data from different sources

What is data parsing?

• Combining data from multiple fields into one

• Separating data from a single field into multiple fields

• Summarizing data for analysis

• Filtering out incorrect data entries

What are data contradiction errors?

• Errors that occur when data is duplicated

• Errors that exist when the same entity is described in two conflicting ways

• Errors that arise from data formatting issues

• Errors that occur during data entry

What is the role of visual inspection in data validation?

• To automate data cleaning processes

• To aggregate data for analysis

• To examine data using human vision for problems

• To perform complex statistical tests