In data science, what is the term for the process of finding and handling missing data in a dataset?

Data visualization
Data aggregation
Data imputation
Data normalization

The correct answer is C. Data imputation.

Data imputation is the process of filling in missing values in a dataset. This can be done using a variety of methods, such as mean imputation, median imputation, or regression imputation. The goal of data imputation is to create a complete dataset that can be used for analysis.

Data visualization is the process of representing data in a graphical or pictorial format. This can be done using a variety of tools, such as charts, graphs, and maps. The goal of data

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visualization is to make data easier to understand and interpret.

Data aggregation is the process of combining data from multiple sources into a single dataset. This can be done using a variety of methods, such as merging, joining, and pivoting. The goal of data aggregation is to create a dataset that is more manageable and easier to analyze.

Data normalization is the process of transforming data so that it has a standard distribution. This can be done using a variety of methods, such as standardization, z-score transformation, and min-max normalization. The goal of data normalization is to make data easier to compare and analyze.