The correct answer is C. Data Visualization.
Data preprocessing is the process of cleaning, transforming, and organizing data so that it can be used for analysis. It is a critical step in the data science process, as it ensures that the data is accurate and complete before it is used for modeling or analysis.
Data cleaning is the process of identifying and correcting errors in data. This can include removing duplicate records, correcting typos, and filling in missing values.
Data transformation is the process of converting data into a format that is more suitable for analysis. This can include converting data from one type to another, such as from text to numeric, or from one format to another, such as from a spreadsheet to a database.
Data integration is the process of combining data from multiple sources into a single dataset. This can be done by merging data from different databases, or by importing data from different files.
Data visualization is the process of creating visual representations of data. This can be done through charts, graphs, and other visuals. Data visualization can be used to communicate insights from data to others, or to help people understand complex data sets.
While data visualization is an important part of the data science process, it is not a common step in data preprocessing. This is because data visualization is typically done after data has been cleaned, transformed, and integrated.